CN113360762A

CN113360762A - Artificial intelligence based content recommendation method and artificial intelligence content recommendation system

Info

Publication number: CN113360762A
Application number: CN202110681659.8A
Authority: CN
Inventors: 阚忠建
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-06-19
Filing date: 2021-06-19
Publication date: 2021-09-07

Abstract

The embodiment of the disclosure provides a content recommendation method and an artificial intelligence content recommendation system based on artificial intelligence, which match a target conversation topic distribution matrix of a conversation service flow and a weighted directed operation service graph of the conversation service flow through a conversation interest path, so as to synthesize interest point mining data of the conversation service flow and the weighted directed operation service graph of the conversation service flow, extract rich open topic relations of the conversation service flow, and thus, efficiently mine key interest objects based on the relations of the open topics in the mining process of the key interest objects; in addition, key interest objects are extracted from the conversation service process through the target conversation topic distribution matrix of the conversation service process, and a key interest object set of the conversation service process is obtained, so that subsequent content recommendation is facilitated, and the pushing precision is improved.

Description

Artificial intelligence based content recommendation method and artificial intelligence content recommendation system

Technical Field

The present disclosure relates to the field of machine learning model technologies, and in particular, to an artificial intelligence based content recommendation method and an artificial intelligence based content recommendation system.

Background

With the development of emerging computing fields such as cloud computing, big data, and the internet of things, the number and types of available services in the internet environment are rapidly increasing. The maturity of service computing technology and its widespread use in various fields has led to a rapid growth in service economy, service market and service business. Therefore, how to provide the user-centered service and the combination technology thereof for the market will become one of the future trends of service calculation, and it is very important and urgent to accurately mine the user preference and make efficient and accurate service recommendation.

In the recommendation system, by modeling the preference of the user, for example, the determination of the attention tendency degree of the user to an article (possibly an e-commerce commodity and the like), content recommendation can be carried out according to the preference so that the recommended content is more matched with the actual portrait characteristics of the user. However, in the related art, there are still many inaccurate places for content recommendation only depending on the attention tendency degree of the user to the article, for example, some algorithms for attention tendency degree do not consider the open topic relationship of the session service flow, and cannot express the key interest objects which are actually more concerned by the user more deeply, so that the content recommendation accuracy rate of the related art still has a relatively large optimization space.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, the present disclosure is directed to providing a content recommendation method and an artificial intelligence content recommendation system based on artificial intelligence.

In a first aspect, the present disclosure provides a content recommendation method based on artificial intelligence, applied to an artificial intelligence content recommendation system, where the artificial intelligence content recommendation system is in communication connection with a plurality of intelligent online service terminals, and the method includes:

acquiring frequent operation item big data of a specified E-commerce service object according to the service attention tendency of the specified E-commerce service object on the current E-commerce commodity conversation page, and performing interest point mining on the frequent operation item big data through an interest point mining strategy to obtain interest point mining data of a conversation service process in the frequent operation item big data;

analyzing a conversation theme distribution matrix based on the interest point mining data of the conversation service flow to obtain a target conversation theme distribution matrix of the conversation service flow;

performing weighted directed operation business graph analysis on the frequent operation item big data based on a machine learning model to obtain a weighted directed operation business graph of the session service flow;

and performing session interest path matching on the target session subject distribution matrix of the session service flow in the frequently-operated item big data and the weighted directed operation business graph of the session service flow to obtain session interest path matching information of the session service flow, and performing key interest object extraction on the frequently-operated item big data based on the session interest path matching information of the session service flow to obtain a key interest object set of the session service flow, wherein the key interest object set is used for recommending e-commerce content.

In a second aspect, an embodiment of the present disclosure further provides an artificial intelligence based content recommendation system, where the artificial intelligence based content recommendation system includes an artificial intelligence content recommendation system and a plurality of intelligent online service terminals in communication connection with the artificial intelligence content recommendation system;

the artificial intelligence content recommendation system is used for:

According to any one of the aspects, in the embodiment provided by the disclosure, the target session topic distribution matrix of the session service flow and the weighted directed operation business graph of the session service flow are matched through the session interest path, so that the weighted directed operation business graph of the session service flow and the interest point mining data of the session service flow are integrated, and rich open topic relations of the session service flow are extracted, thereby facilitating efficient key interest object mining based on the relations of the open topics in the key interest object mining process; in addition, key interest objects are extracted from the conversation service process through the target conversation topic distribution matrix of the conversation service process, and a key interest object set of the conversation service process is obtained, so that subsequent content recommendation is facilitated, and the pushing precision is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present disclosure, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic view of an application scenario of an artificial intelligence based content recommendation system according to an embodiment of the present disclosure;

FIG. 2 is a flowchart illustrating an artificial intelligence based content recommendation method according to an embodiment of the disclosure;

FIG. 3 is a functional block diagram of an artificial intelligence based content recommendation apparatus according to an embodiment of the disclosure;

fig. 4 is a block diagram illustrating a structure of an artificial intelligence content recommendation system for implementing the artificial intelligence based content recommendation method according to the embodiment of the present disclosure.

Detailed Description

The following describes in detail aspects of embodiments of the present disclosure with reference to the drawings attached hereto. In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the particular embodiments of the disclosure.

Fig. 1 is a scene diagram of an artificial intelligence based content recommendation system 10 according to an embodiment of the present disclosure. The artificial intelligence based content recommendation system 10 may include an artificial intelligence content recommendation system 100 and an intelligent online service terminal 200 communicatively connected to the artificial intelligence content recommendation system 100. The artificial intelligence based content recommendation system 10 shown in FIG. 1 is only one possible example, and in other possible embodiments, the artificial intelligence based content recommendation system 10 may also include only at least some of the components shown in FIG. 1 or may also include other components.

In this embodiment, the artificial intelligence content recommendation system 100 and the intelligent online service terminal 200 in the artificial intelligence based content recommendation system 10 can cooperatively execute the artificial intelligence based content recommendation method described in the following method embodiments, and the following detailed description of the method embodiments can be referred to for the execution steps of the artificial intelligence content recommendation system 100 and the intelligent online service terminal 200.

In order to solve the technical problem in the foregoing background art, fig. 2 is a flowchart illustrating an artificial intelligence based content recommendation method provided in an embodiment of the present disclosure, where the artificial intelligence based content recommendation method provided in this embodiment can be executed by the artificial intelligence based content recommendation system 100 shown in fig. 1, and the artificial intelligence based content recommendation method is described in detail below.

Step S110, according to the service attention tendency of the appointed e-commerce service object on the current e-commerce commodity conversation page, obtaining the frequent operation item big data of the appointed e-commerce service object, and performing interest point mining on the frequent operation item big data through an interest point mining strategy to obtain interest point mining data of a conversation service process in the frequent operation item big data.

For example, frequent pattern item mining can be performed on operation big data of the specified e-commerce service object, of which the service attention tendency of the current e-commerce commodity conversation page is greater than a preset threshold value, so as to obtain frequent operation item big data of the specified e-commerce service object.

Step S120, analyzing the distribution matrix of the conversation theme based on the interest point mining data of the conversation service process to obtain a target conversation theme distribution matrix of the conversation service process.

And S130, performing weighted directed operation business graph analysis on the frequent operation item big data based on the machine learning model to obtain a weighted directed operation business graph of the session service process.

Step S140, carrying out session interest path matching on the target session subject distribution matrix of the session service flow in the frequently operated item big data and the weighted directed operation service graph of the session service flow to obtain session interest path matching information of the session service flow, and carrying out key interest object extraction on the frequently operated item big data based on the session interest path matching information of the session service flow to obtain a key interest object set of the session service flow, wherein the key interest object set is used for E-commerce content recommendation.

In this embodiment, the interest point mining policy may be understood as a function module that needs to be used to mine the interest points, and then perform this operation. The operation of the function module related to specific interest point mining can be referred to the following detailed description of step S110.

In this embodiment, the frequent operation item big data may be understood as a data set generated by each session service flow generated by a frequent item algorithm. The session service flow may refer to a certain session flow formed under the big data of the frequently-operated item.

In this embodiment, the session topic distribution matrix may be used to represent thermal distribution information of a session topic corresponding to each session service flow, and the weighted directed operation service graph may be used to describe operation service relationship information for each operation service in a service operation process.

In the embodiment, the target session topic distribution matrix of the session service flow and the weighted directed operation service graph of the session service flow are matched through the session interest path, the weighted directed operation service graph of the session service flow and the interest point mining data of the integrated session service flow are used for extracting rich open topic relations of the session service flow, so that efficient key interest object mining is performed based on the open topic relations in the key interest object mining process; in addition, key interest objects are extracted from the conversation service process through the target conversation topic distribution matrix of the conversation service process, and a key interest object set of the conversation service process is obtained, so that subsequent content recommendation is facilitated, and the pushing precision is improved.

Some alternative embodiments of the present application will be described below, and it should be understood that the following description of the embodiments is only an example, and should not be construed as an essential technical feature for implementing the present solution.

In one embodiment, for step S110, in the process of performing interest point mining on the frequently-operated item big data through the interest point mining policy to obtain the interest point mining data of the session service flow in the frequently-operated item big data, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S111, obtaining the operation behavior updating fragment sequence generated by the commodity conversation service of each frequent operation item data in the frequent operation item big data.

In this embodiment, it is worth to be noted that the operation behavior update fragment sequence includes an operation behavior update fragment in which each e-commerce commodity label is taken as an operation object, and the operation behavior update fragment includes operation trigger information and an operation trigger result of the e-commerce commodity label and an operation change log in the e-commerce commodity label.

Substep S112, for each E-commerce commodity label, according to each change unit log in a plurality of change unit logs in the operation change log of the E-commerce commodity label of each frequent operation item data, according to the record identification of the operation service record information in the change unit log, determining whether each operation service record information in the change unit log is a key operation service record, according to the record category of the key operation service record in the change unit log, determining the session interest information of each session service data packet corresponding to the change unit log, for the session interest information of each session service data packet, dividing the session interest information of the session service data packet into a plurality of sub-session interest information, according to the interest source and preset interest source range of each operation service record information in each sub-session interest information, and determining whether the session interest information of the session service data packet is the session interest information of the target preset legal interest source.

It is worth mentioning that each operation service record information corresponds to each operation change behavior.

And a substep S113, obtaining interest description feature information of each operation service record information in the session interest information of the preset interest description strategy matching the target preset legal interest source, wherein the interest description feature information comprises interest description capture information and interest description application information, and the preset interest description strategy comprises description strategies corresponding to different interest description modes.

Substep S114, determining the dynamic attribute information of the dynamic scene attribute and the static attribute information of each static scene attribute of each interest description according to the interest description feature information of each operation change log of different E-commerce commodity labels in the operation behavior update segment sequence, determining the interest description label object of each frequent operation item data in the E-commerce commodity label according to the dynamic attribute information of the dynamic scene attribute and the static attribute information of each static scene attribute of each interest description in the conversation interest information of the target preset legal interest source, taking the interest point information in the description range of the interest description label object and the interest point information outside the description range of the interest description label object and related to the description range of the interest description label object as the interest point information of each frequent operation item data in the E-commerce commodity label, and after the interest point information of each frequently-operated item data in all E-commerce commodity labels is gathered, interest point mining data of the session service process in the frequently-operated item big data is obtained.

For example, a static scene attribute refers to a scene attribute whose value remains constant throughout the operation. For another example, a dynamic scene attribute refers to a scene attribute whose value changes throughout the operation.

In one embodiment, for step S120, in the process of performing session topic distribution matrix analysis based on the interest point mining data of the session service flow to obtain a target session topic distribution matrix of the session service flow, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S121, obtaining topic embedding vector reference information of the topic embedding vector sequence marked on the associated topic information of each interest point information in the interest point mining data of the session service process, and determining a first topic participation behavior set corresponding to the topic embedding vector reference information.

It is worth to be noted that the topic embedding vector reference information includes embedding vector information of topic vector calculation relationship information determined according to topic vector input information and topic vector output information of the topic embedding vector sequence, and the first topic participation behavior set includes a high-low order of priorities of a plurality of topic participation behaviors of the embedding vector information.

And a substep S122 of determining associated topic information of each point of interest information based on a first topic vector component of the topic vector input information and based on a second topic vector component of the topic vector output information.

And a substep S123 of determining first analysis information for performing K-nearest neighbor algorithm analysis on the first topic participation behavior set according to the topic participation behavior priority relationship between the first topic vector component and the second topic vector component.

And a substep S124, performing K nearest neighbor algorithm analysis on the first topic participation behavior set based on the first analysis information to obtain a second topic participation behavior set.

And a substep S125, performing topic activity category clustering on the second topic participation behavior set to obtain a plurality of topic activity category clustering sets, and performing feature extraction on each topic activity category clustering set to obtain topic activity category clustering features.

And a substep S126, determining a conversation topic distribution matrix of each interest point information according to a conversation topic distribution matrix corresponding to a plurality of topic activity category clustering features corresponding to the second topic participation behavior set.

And a substep S127 of obtaining a target conversation topic distribution matrix of the conversation service process based on the conversation topic distribution matrix of each interest point information.

Further, in an embodiment, for step S130, in the process of performing weighted directed operation business graph analysis on the big data of the frequently-operated item based on the machine learning model to obtain the weighted directed operation business graph of the session service flow, the weighted directed operation business graph may be implemented by the following exemplary sub-steps, which are described in detail below.

And a substep S131, inputting the big data of the frequent operation item into a preset machine learning model, and obtaining the matching degree of the big data of the frequent operation item matched with each preset directed service node.

And a substep S132, determining a target directed service node corresponding to the frequent operation item big data according to the matching degree of the frequent operation item big data matched with each preset directed service node.

For example, a preset directed service node with a matching degree greater than a preset matching degree threshold may be determined as a target directed service node corresponding to the big data of the frequent operation item.

And a substep S133, extracting a weighted directed operation business graph matched with each session service flow from the directed business graph information of the target directed business node corresponding to the big data of the frequent operation item.

In one embodiment, for step S140, in the process of performing session interest path matching on the target session topic distribution matrix of the session service flow in the frequent operation item big data and the weighted directed operation business graph of the session service flow to obtain session interest path matching information of the session service flow, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S141, adding the target session theme distribution matrix and the weighted directed operation service graph of the session service flow into the session interest path matching network, and determining the target session theme distribution matrix and the path corresponding information of the weighted directed operation service graph of the session service flow corresponding to each preset session service path from the session interest path matching network.

And a substep S142, clustering the information corresponding to each path according to the interest source of the path relation between the preset session service paths in the information corresponding to each different path, so as to obtain at least one path corresponding information cluster.

In this embodiment, the interest source of the path relationship of the preset session service path in any two pieces of path corresponding information in the same path corresponding information cluster covers the preset interest source range.

And a substep S143, for each path corresponding information cluster, determining a target session topic distribution matrix of the path corresponding information cluster for the session service flow and unit path matching information corresponding to the weighted directed operation service graph from the session interest path matching network based on each path corresponding information in the path corresponding information cluster.

In this embodiment, the unit path matching information at least includes a target session topic distribution matrix of each path corresponding information in the path corresponding information cluster for the session service flow and feature information data of each topic hopping relationship of the weighted directed operation service graph, and the unit path matching information is used to determine a target session topic distribution matrix of a session service flow corresponding to a preset session service path in each path corresponding information in the path corresponding information cluster and topic output information of a topic relationship of the weighted directed operation service graph.

And a substep S144 of determining subject output information of a subject relation corresponding to a preset session service path in each path corresponding information in the path corresponding information cluster based on the unit path matching information, performing path matching on the subject output information of the subject relation to obtain path matching information, and obtaining session interest path matching information of the session service process according to the path matching information.

For example, in a possible example, for the sub-step S144, in the process of determining the topic output information of the topic relationship corresponding to the preset session service path in each path corresponding information in the path corresponding information cluster based on the unit path matching information, the following alternative implementation may be implemented.

And a substep S1441, determining topic rule information and topic access categories of unit path matching information, determining a plurality of topic access combinations according to a plurality of path objects in a historical path object set, mining topic potential extension features according to topic parameters of each path object in each topic access combination and the topic access categories in the topic rule information to obtain a plurality of topic potential extension feature mining results respectively corresponding to the plurality of topic access combinations, and taking access auxiliary parameters of the topic access combination corresponding to each topic potential extension feature mining result as topic access auxiliary parameters of each topic potential extension feature mining result.

And a substep S1442, based on the plurality of topic potential extension feature mining results, obtaining topic path rules respectively determining information corresponding to each path corresponding to the topic access category in the topic rule information to obtain a plurality of topic path rules, and according to topic access auxiliary parameters of each topic potential extension feature mining result, integrating the plurality of topic path rules obtained based on the plurality of topic potential extension feature mining results to obtain a first topic path rule set.

It should be noted that the access affiliation parameters between any two path objects in each topic access combination are the same, the access affiliation parameters corresponding to different topic access combinations are different, each topic potential extension feature mining result is used for determining common open topic information corresponding to a preset session service path in each path corresponding information corresponding to the topic access category in any set service interval, and the topic potential extension feature mining result is obtained by performing topic potential extension feature mining according to a historical path object set.

And a substep S1443, obtaining an open topic result of the preset session service paths between the subject rule information and the subject access category according to the common open topic information of the preset session service paths corresponding to the historical path object set and the subject access category, and taking a target node corresponding to the open topic result corresponding to the preset session service paths as a second subject path rule.

And a substep S1444 of comparing the theme path related information of the first theme path rule set and the second theme path rule, and determining theme output information of the theme relationship corresponding to each preset conversation service path according to the theme path related information and the common open theme information of each preset conversation service path.

For example, in an alternative embodiment, for sub-step S1444, the following embodiment may be implemented.

(1) Determining invitation scene attribute information of the E-commerce interaction scene corresponding to each preset session service path based on the theme path related information, and determining session scene theme matching information of each preset session service path according to the theme matching relationship of the common open theme information of each preset session service path in the corresponding path corresponding information.

(2) Extracting a first conversation theme distribution matrix sequence corresponding to the invitation scene attribute information and a second conversation theme distribution matrix sequence corresponding to the conversation scene theme matching information, and determining a plurality of target distribution information with different conversation theme characteristic segments respectively included in the first conversation theme distribution matrix sequence and the second conversation theme distribution matrix sequence.

The target distribution information may be understood as a distribution formed by the conversation topic distribution matrixes having an association relationship in the first conversation topic distribution matrix sequence and the second conversation topic distribution matrix sequence.

(3) On the premise that the distribution path attribute of the first conversation topic distribution matrix sequence is the same as the distribution path attribute of the second conversation topic distribution matrix sequence, acquiring the reference relation information of the invitation scene attribute information in any target distribution information of the first conversation topic distribution matrix sequence, and determining the target distribution information with the smallest conversation topic characteristic segment in the second conversation topic distribution matrix sequence as the updated target distribution information in parallel.

(4) And adding the reference relation information to the updated target distribution information based on the extraction strategy of each preset session service path, and determining data dictionary information corresponding to the reference relation information in the updated target distribution information.

(5) And generating a matching node between the invitation scene attribute information and the conversation scene subject matching information by using a reference relation dictionary object between the reference relation information and the data dictionary information.

(6) And obtaining target reference distribution information in the updated target distribution information by taking the data dictionary information as reference information, adding the target reference distribution information to the target distribution information where the reference relation information is located according to a plurality of matching service paths corresponding to the matching nodes, obtaining a subject abstract type result corresponding to the target reference distribution information in the target distribution information where the reference relation information is located, and determining the subject abstract type result as subject editing information according to the reference information.

(7) And determining a corresponding path relation result when the reference relation information is added to the updated target distribution information.

(8) According to the association degree between the subject abstract class result and the path relation feature labels corresponding to the path relation tracing nodes in the path relation result, sequentially acquiring the theme program code labels corresponding to the theme editing information in the second conversation theme distribution matrix sequence according to the sequence of the time sequence weight from large to small until the number of the software frame update nodes of the target distribution information where the acquired theme program code labels are located is consistent with the number of the software frame update nodes of the theme editing information in the first conversation theme distribution matrix sequence, stopping acquiring the theme program code labels in the next target distribution information, and establishing traceability associated information between the theme editing information and the last acquired theme program coding label, and determining theme output information of the theme relationship corresponding to each preset session service path based on the traceability associated information.

In one embodiment, still referring to step S140, in the process of performing key interest object extraction on the frequently-operated item big data based on the session interest path matching information of the session service flow to obtain a key interest object set of the session service flow, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S145, acquiring key interest object extraction information of the session service flow under the big data of the frequent operation items.

And a substep S146, obtaining the key interest objects under the key interest object extraction information and the topic interest path information corresponding to each key interest object.

And a substep S147, covering and configuring the session interest path matching information of the session service flow under the topic interest path information corresponding to each key interest object, and obtaining a key interest object set of the session service flow.

For example, in one embodiment, after step S140, the following steps may be further included:

and step S150, acquiring a recommended content data set obtained based on the key interest object sets of different session service processes.

Step S160, acquiring entity attribute information of a plurality of knowledge-graph entities in a recommended content knowledge graph of the recommended content data set.

In this embodiment, each knowledge-graph entity may be configured to represent one or more push tasks that need to be performed during the pushing process of the recommended content data set, and the recommendation timing information of the push task represented by each knowledge-graph entity needs to be updated.

In this embodiment, the entity attribute information of any one of the knowledge-graph entities is used to reflect the association relationship between any one of the knowledge-graph entities and other knowledge-graph entities.

Step S170, clustering at least two knowledge graph entities into a target clustering entity set according to the entity attribute information of each knowledge graph entity.

In this implementation, the target clustering entity set is used to instruct to update the recommended timing information of the push task represented by the clustered knowledge graph entities.

And step S180, updating the recommended content knowledge graph by adopting the target clustering entity set, and sending the updated recommended content knowledge graph to an information pushing service.

In this implementation, the updated recommended content knowledge graph may be used to indicate the information push service to update the recommended timing information of the push task represented by the clustered knowledge graph entity in the push process of the recommended content data set according to the indication of the target clustered entity set, and output the update result.

In detail, in some possible implementation manners, the recommended timing information may be configured according to requirements of actual software functions, and may specifically be customized, or refer to a conventional test task in the prior art, which is not limited herein. In addition, the specific test logic of the information push service can be configured adaptively by referring to the recommended timing information, and the content and form of the specific test are not the technical problems that the embodiments of the present application aim to solve, and are not described in detail herein.

Based on the above steps, in this embodiment, at least two knowledge graph entities may be clustered into a target clustering entity set according to entity attribute information of a plurality of knowledge graph entities in a recommended content knowledge graph of a recommended content data set, where the target clustering entity set is used to indicate that recommended timing information of a push task represented by the clustered knowledge graph entities is updated. Then, the recommended content knowledge graph can be updated by adopting the target clustering entity set, and the updated recommended content knowledge graph is sent to the information pushing service, so that the information pushing service can update the recommended time sequence information of the pushing tasks represented by the clustered knowledge graph entities according to the indication of the target clustering entity set in the process of testing the recommended content data set, and the pushing precision and the pushing pertinence are improved.

In one embodiment, in the foregoing step S110, the service attention tendency of the e-commerce service object on the current e-commerce commodity conversation page is specified, which may be implemented by the following steps.

Step A101, obtaining the conversation behavior characteristics of the appointed e-commerce service object on the current e-commerce commodity conversation page.

In this embodiment, the session behavior characteristics may include session skip information corresponding to the current e-commerce commodity session page and session task characteristics of the processed session task.

In this embodiment, the current e-commerce commodity conversation page corresponds to the conversation skip information updated by the current business concern tendency, and the conversation skip information corresponding to the current e-commerce commodity conversation page is also the conversation skip information updated by the current business concern tendency. Considering that the conversation tasks of different pages are different, even if the attention tendencies of the marking services required by the same conversation task flow are different, the conversation skip information corresponding to the conversation page of the current e-commerce commodity is collected as an important forming characteristic in the method.

In this embodiment, the processed conversation task is a commodity interaction conversation task that designates an e-commerce service object from a service node where the current e-commerce commodity conversation page is located to the processed node. For example, the initial commodity interaction session task generated by the designated e-commerce service object at the current service node is A-B-C-D, that is, the complete commodity interaction session task is a session task set formed by starting from A, successively passing through the B session task and the C session task, and finally jumping to the D session task, after the designated e-commerce service object jumps to the B session task at a certain e-commerce commodity session page, the processed session task at the e-commerce commodity session page is B-C-D, and if the designated e-commerce service object plans the commodity interaction session task again at the e-commerce commodity session page, the processed session task is the regenerated commodity interaction session task.

The session task characteristic may be one or more of a session click behavior characteristic, a session browsing behavior characteristic, a session subscription behavior characteristic, a session invitation behavior characteristic and a session screenshot behavior characteristic, which is not limited in particular.

Step A102, inputting the conversation behavior characteristics of the appointed e-commerce service object on the current e-commerce commodity conversation page into a pre-trained service attention tendency prediction model, and obtaining the service attention tendency of the current e-commerce commodity conversation page output by the service attention tendency prediction model.

After the conversation behavior characteristics of the current E-commerce commodity conversation page are obtained, the service attention tendency of the appointed E-commerce service object on the current E-commerce commodity conversation page can be obtained by inputting the driving characteristics of the current E-commerce commodity conversation page into the pre-trained service attention tendency prediction model.

The business concern tendency prediction model of the embodiment of the present disclosure is trained in a network weight update manner, and generally, for the training process of the network weight update model, in order to obtain a better strategy through training, continuous interaction with a training unit is required through a machine learning network. In the disclosed embodiment, the machine learning network can be understood as a business concern tendency prediction model.

For example, the machine learning network can output a prediction result through the prediction network layer and act on the training unit, the training unit receives the prediction result and then the training characteristics change, meanwhile, model weight index information is generated according to the model weight index, the training unit feeds the current training characteristics and the model weight index information back to the machine learning network, the machine learning network outputs the next prediction result according to the model weight index information and the current training characteristics of the training unit, and the principle of outputting the prediction result is that the probability of receiving the forward model weight index information is increased. The selected prediction result not only influences the current model weight index information, but also influences the training characteristics of next session skip information of the training unit and the final model weight index information, thereby realizing a circular training response process.

The model weight index information may refer to a loss function value. In the service attention tendency prediction process of the embodiment of the disclosure, the model weight index information is obtained through the model weight index, the model weight index information can be divided into two parts, the first part is the service attention tendency decision precision estimated by each E-commerce commodity conversation page, and the second part is the change tendency information of the sequence formed by the service attention tendency estimated by the current E-commerce commodity conversation page and the service attention tendency of all the previous E-commerce commodity conversation pages.

The indexes of the model weight indexes of the service attention tendency prediction model in the embodiment of the disclosure are based on the marked service attention tendency including the marked data set and the past predicted service attention tendency set of each e-commerce commodity conversation page, and the output of the model weight indexes is used for representing the evaluation information of the decision precision of the predicted service attention tendency of each e-commerce commodity conversation page. The past predicted business concern tendency set comprises predicted business concern tendencies of at least one E-commerce commodity conversation page related to each E-commerce commodity conversation page; the predicted business concern tendency of each E-commerce commodity conversation page is obtained according to classification result information of the business concern tendency predicted by the conversation behavior characteristics of each E-commerce commodity conversation page in the network weight updating process of the prediction network layer of the business concern tendency prediction model.

For example, the business concern tendency prediction model may include a prediction network layer and a model weight index, the session behavior feature of each e-commerce commodity session page in the annotation data set is a training feature, if there are T e-commerce commodity session pages in the annotation data set, there are T training features, each training feature is used as an input of the prediction network layer, and the prediction network layer outputs a prediction result based on the input training features: classification result information of service attention tendency of each E-commerce commodity conversation page; screening information can be obtained by randomly screening the classification result information of the service attention tendency of each E-commerce commodity conversation page: the predicted business attention tendency of each E-commerce commodity conversation page, aiming at each E-commerce commodity conversation page, the predicted business attention tendency of at least one E-commerce commodity conversation page related to the E-commerce commodity conversation page forms a past predicted business attention tendency set, the marked business attention tendency and the past predicted business attention tendency set of each E-commerce commodity conversation page are used as index basis of a model weight index, the model weight index generates evaluation information for evaluating the decision accuracy of the predicted business attention tendency of each E-commerce commodity conversation page on the basis of input, and a prediction network layer in a business attention tendency prediction model is adjusted on the basis of the evaluation information, so that the output probability of the predicted business attention tendency with good evaluation information is increased, and the output probability of the prediction service attention tendency of the evaluation information difference is reduced, so that the trained prediction network layer learns the correct service attention tendency prediction behavior.

It should be noted that the indexes of the model weight indexes of the embodiment of the present disclosure are based on the set of the labeling business concern tendencies including the labeling data set and the past predicted business concern tendencies of each e-commerce commodity conversation page. The marked business concern tendency provides a basis for evaluating the decision precision of the predicted business concern tendency of each E-commerce commodity conversation page, and the past predicted business concern tendency set of each E-commerce commodity conversation page is constructed, and the trend change condition of the business concern tendency is considered, namely, the model weight index of the embodiment of the disclosure can evaluate the business concern tendency from two aspects of decision precision and trend, thereby laying a foundation for predicting the business concern tendency which is more in line with high decision precision and user habits in practical application.

The content recommendation method based on artificial intelligence of the embodiment of the disclosure obtains the conversation behavior characteristics of the appointed e-commerce service object on the current e-commerce commodity conversation page, wherein the conversation behavior characteristics comprise the conversation skip information corresponding to the current e-commerce commodity conversation page and the conversation task characteristics of the processed conversation task, so that a business concern tendency prediction model can more accurately predict business concern tendency according to the conversation skip information and the conversation task, more importantly, indexes of model weight indexes during the training of the business concern tendency prediction model comprise business concern tendency and past prediction business concern tendency of each e-commerce commodity conversation page And (4) updating, namely considering the influence of the continuous change of the session task on the prediction of the service attention tendency, so that the prediction result is more accurate.

On the basis of the above embodiments, in one embodiment, inputting the session behavior characteristics of the specified e-commerce service object on the current e-commerce commodity session page into a pre-trained service attention tendency prediction model, and obtaining the service attention tendency of the current e-commerce commodity session page output by the service attention tendency prediction model, the method includes:

step A1021, performing convolution feature extraction on the conversation behavior feature of the current E-commerce commodity conversation page to obtain the conversation behavior convolution feature of the current E-commerce commodity conversation page. It will be appreciated that the conversational behavior convolution feature is a behavioral description representation of the conversational behavior feature.

Step A1022, inputting the conversation behavior convolution characteristics of the current e-commerce commodity conversation page to a prediction network layer, and obtaining classification result information of the business concern tendency of the current e-commerce commodity conversation page output by the prediction network layer;

and A1023, screening according to the classification result information of the business concern tendency of the current E-commerce commodity conversation page, and obtaining the business concern tendency of the current E-commerce commodity conversation page.

In one embodiment, a training process of a business concern tendency prediction model according to an embodiment of the present disclosure is described below, where the training process includes:

step A201, acquiring the conversation behavior characteristics and the business attention tendency of each E-commerce commodity conversation page of the annotation data set.

In the embodiment of the present disclosure, each training unit is a one-time complete session initiation process, that is, a labeled data set, where one labeled data set includes a session behavior feature and a final business concern tendency of each e-commerce commodity session page in the session initiation process.

For example, assuming that a sample user initiates a session task at a current service node a, a commercial product session page is 1, a session behavior feature is recorded as X1, and a target session task is C, if the sample user continuously updates a service attention tendency in a session initiation process, when the sample user jumps to a session task B, a corresponding commercial product session page is n, a session behavior feature is recorded as Xn, and if the number of commercial product session pages in the whole session initiation process is T, the annotation data set can be recorded as { X1, X2, …, Xn,. and.. XT }, where n and T are positive integers, and n is less than T.

Step A202, inputting the conversation behavior characteristics of each e-commerce commodity conversation page into a prediction network layer of a business concern tendency prediction model to be trained, and obtaining classification result information of the business concern tendency of each e-commerce commodity conversation page output by the prediction network layer.

The prediction network layer of the embodiment of the disclosure outputs probability values of various prediction results executed under training characteristics based on the idea of a policy gradient algorithm, namely classification result information of service attention tendency under the conversation behavior characteristics of each E-commerce commodity conversation page

For example, the conversation behavior feature Xn of the nth e-commerce commodity conversation page may be input to the prediction network layer, and the probability that the business concern tendency of the nth e-commerce commodity conversation page output by the prediction network layer is the business concern tendency n1, the probability of the business concern tendency n2, …, and the probability of the business concern tendency nm, where the business concern tendency nm represents the mth predicted value of the business concern tendency n. The function of the neural network output layer at this time is similar to the step of the multiple classification problem, namely, the step of the Aofmax regression, and the output is the classification result information, except that the classification result information is not used for classification.

Step A203, screening according to the classification result information of the business concern tendency, and obtaining the predicted business concern tendency of each E-commerce commodity conversation page.

In this embodiment, random screening is performed according to the classification result information of the business concern tendency, and the screening value is used as the predicted business concern tendency of each e-commerce commodity conversation page.

Step A204, inputting a model weight index layer according to the business concern tendency and the past predicted business concern tendency set of each E-commerce commodity conversation page, and obtaining model weight index information of each E-commerce commodity conversation page output by the model weight index layer.

Step A205, according to the model weight index information of each E-commerce commodity conversation page and the classification result information of the predicted business concern tendency, performing weight updating on the network weight of the predicted network layer, and taking the trained predicted network layer as a target business concern tendency prediction model.

For example, step a205 further includes:

and obtaining the total model weight index information of each E-commerce commodity conversation page according to the sum of the model weight index information of all E-commerce commodity conversation pages behind each E-commerce commodity conversation page.

And according to the total model weight index information of each E-commerce commodity conversation page and the classification result information of the business concern tendency, carrying out weight updating on the network weight of the prediction network layer by a gradient descent method.

On the basis of the above embodiments, in one embodiment, inputting a model weight index layer according to a service attention tendency and a past predicted service attention tendency set of each e-commerce commodity conversation page to obtain model weight index information of each e-commerce commodity conversation page output by the model weight index layer includes:

step A301, inputting the predicted business concern tendency and the business concern tendency of each E-commerce commodity conversation page into a model weight index layer, and obtaining decision precision model weight index information of the predicted business concern tendency of each E-commerce commodity conversation page output by the model weight index layer; the decision precision model weight index information is used for representing the prediction precision of the prediction service attention tendency of each E-commerce commodity conversation page;

in the process of calculating the model weight index information, the model weight index information is divided into decision precision model weight index information used for representing the prediction precision of the predicted business concern tendency of each e-commerce commodity conversation page and trend model weight index information used for representing the change trend information of the predicted business concern tendency of each e-commerce commodity conversation page relative to the past predicted business concern tendency set.

For the decision accuracy model weight index information, the evaluation is performed according to the predicted business concern tendency and business concern tendency of each e-commerce commodity conversation page, for example:

step A301a, determining the actual tendency of the processed conversation task of each E-commerce commodity conversation page according to the conversation jump information and the business attention tendency corresponding to each E-commerce commodity conversation page, wherein the conversation jump information corresponding to each E-commerce commodity conversation page is recorded by the annotated data set, and the business attention tendency records the actual attention tendency preference, so the actual tendency of the processed conversation task can be obtained according to the two information.

Step A301b, determining the difference value between the predicted business concern tendency of each E-commerce commodity conversation page and the actual tendency of the processed conversation task, and obtaining the decision precision model weight index information of each E-commerce commodity conversation page according to the difference value.

Since a smaller difference between the actual tendency and the predicted business attention tendency means a higher decision precision of the business attention tendency, the embodiment of the present disclosure may determine the decision precision model weight index information of different differences according to different ranges, for example, the difference is 1 in 0-1 minute of the model weight index information, and the difference is 0 in more than 1 minute of the model weight index information, so the decision precision model weight index information of the tth e-commerce commodity conversation page is 0. It should be noted that the number of ranges and the specific numerical value of the model weight index information corresponding to different ranges are not specifically limited in the present disclosure.

Step A302, inputting the predicted business concern tendency and the past predicted business concern tendency set of each E-commerce commodity conversation page into a model weight index layer to obtain trend model weight index information of each E-commerce commodity conversation page output by the model weight index layer; the trend model weight index information is used for representing the change trend information of the predicted business concern tendency of each E-commerce commodity conversation page relative to the past predicted business concern tendency set.

The method and the device for evaluating the business concern tendency prediction decision accuracy further need to evaluate the change tendency information of the business concern tendency set. For example:

step A302a, for any one E-commerce commodity conversation page in at least one E-commerce commodity conversation page related to each E-commerce commodity conversation page, determining the magnitude relation between the predicted business concern tendencies of any one E-commerce commodity conversation page and the previous E-commerce commodity conversation page adjacent to any one E-commerce commodity conversation page.

Step A302b, if it is determined that the predicted service attention tendency of any E-commerce commodity conversation page is greater than the predicted service attention tendency of the previous E-commerce commodity conversation page adjacent to any E-commerce commodity conversation page, the trend model weight component index of any E-commerce commodity conversation page is a first preset value; and if the predicted service attention tendency of any E-commerce commodity conversation page is not larger than the predicted service attention tendency of the previous E-commerce commodity conversation page adjacent to any E-commerce commodity conversation page, the trend model weight component index of each E-commerce commodity conversation page is a second preset value, and the first preset value is smaller than the second preset value.

Step A302c, obtaining trend model weight index information of each E-commerce commodity conversation page according to the trend model weight component indexes of all E-commerce commodity conversation pages in at least one E-commerce commodity conversation page related to each E-commerce commodity conversation page.

Step A303, combining the decision precision model weight index information and the trend model weight index information of each E-commerce commodity conversation page to obtain the model weight index information of each E-commerce commodity conversation page.

That is, when calculating the trend model weight index information, for each e-commerce commodity conversation page, first, a magnitude relationship between predicted business attention tendencies of any two adjacent e-commerce commodity conversation pages is determined, for example, if calculating the trend model weight index information of the 5 th e-commerce commodity conversation page, a magnitude relationship between business attention tendency 5 and business attention tendency 4, a magnitude relationship between business attention tendency 4 and business attention tendency 3, a magnitude relationship between business attention tendency 3 and business attention tendency 2, and a magnitude relationship between business attention tendency 2 and business attention tendency 1 are respectively determined, where the business attention tendency n represents the predicted business attention tendency of the nth e-commerce commodity conversation page.

If the business concern tendency 5 is determined to be greater than the business concern tendency 4, the trend model weight component index of the business concern tendency 5 is 0, and if the business concern tendency 5 is determined to be less than the business concern tendency 4, the trend model weight component index of the business concern tendency 5 is 1. It should be noted that the embodiment of the present disclosure does not specifically limit the specific value of the trend model weight component index. Based on the same calculation mode, if the trend model weight component indexes of the business concern tendency 2 to the business concern tendency 4 are calculated to be 0, 1 and 1, the trend model weight component index of the 5 th e-commerce commodity conversation page may be 0+1+1+1= 3. Of course, in addition to calculating the trend model weight index information in a manner of summing the trend model weight component indexes of all e-commerce commodity conversation pages, the embodiment of the present disclosure may further calculate an average value of the summed result as the trend model weight index information, and may further calculate the trend model weight index information in a manner of weighted summing and re-averaging.

On the basis of the above embodiments, the weight updating of the network weight of the predicted network layer includes:

step A401, dividing all network weights of a prediction network layer into a first classification network weight and a second classification network weight, wherein the first classification network weight and the second classification network weight do not have the same network weight;

step A402, generating a conversation behavior convolution characteristic according to the conversation behavior characteristic;

step A403, fitting a mean value of normal distribution according to an inner product of the session behavior convolution characteristic and the first classification network weight, and fitting a standard deviation of the normal distribution according to an inner product of the session behavior convolution characteristic and the second classification network weight to complete training of the network weight of the prediction network layer.

On the basis of the above embodiments, the conversation behavior feature of the embodiment of the present disclosure may further include a business concern tendency of each e-commerce commodity conversation page related to the current e-commerce commodity conversation page. By taking the service attention tendency of each E-commerce commodity conversation page related to each E-commerce commodity conversation page as the conversation behavior feature, the service attention tendency prediction model can repeatedly learn the dynamic change information of the service attention tendency of each E-commerce commodity conversation page in the prediction process, so that the accuracy of service attention tendency prediction is improved.

Since each e-commerce commodity conversation page related to each e-commerce commodity conversation page is constantly changed, the business concern tendency of each e-commerce commodity conversation page is that the convolution feature of the conversation behavior after being taken as the conversation behavior feature is no longer a fixed-length vector, in this case, the linear prediction network layer is no longer applied, but a more complex model such as a recurrent neural network, a long-short term memory network and the like can be applied, but not limited thereto.

For example, according to the structural description of the business concern tendency prediction model to be trained provided by another embodiment of the present disclosure, an input layer of the business concern tendency prediction model is used for receiving the conversation behavior characteristics and the business concern tendency of an input annotation data set, the conversation behavior characteristics include the conversation skip information of each e-commerce commodity conversation page and the conversation task characteristics of the processed conversation task, and may further include the business concern tendency of each e-commerce commodity conversation page related to each e-commerce commodity conversation page;

the service attention tendency prediction model can utilize the service attention tendency prediction layer to take the conversation behavior characteristics of each step as input and output the predicted service attention tendency of each E-commerce commodity conversation page, specifically, the characteristic extraction layer extracts the conversation behavior characteristics and the conversation behavior convolution characteristics of the service attention tendency, then the classification result information acquisition layer processes the conversation behavior convolution characteristics of the conversation behavior characteristics to obtain the classification result information of the service attention tendency of each E-commerce commodity conversation page, and then the screening layer randomly adopts the classification result information of the service attention tendency of each E-commerce commodity conversation page to obtain the predicted service attention tendency of each E-commerce commodity conversation page.

Obtaining model weight index information of each e-commerce commodity conversation page by taking the business concern tendency and the predicted business concern tendency as input through the model weight index determination layer, for example: outputting decision precision model weight index information of the predicted service attention tendency of each E-commerce commodity conversation page by taking the conversation behavior convolution characteristics of the predicted service attention tendency and the service attention tendency of each E-commerce commodity conversation page as input through an accurate reward calculation layer; and also obtains past forecast service concern tendency sets of each E-commerce commodity conversation page through the information fusion layer according to the forecast service concern tendency of each E-commerce commodity conversation page, wherein the past forecast service concern tendency sets of each E-commerce commodity conversation page are sequentially arranged according to the E-commerce commodity conversation page sequence, the method comprises the steps of obtaining a prediction service attention tendency of at least one E-commerce commodity conversation page related to each E-commerce commodity conversation page, further taking the prediction service attention tendency of each E-commerce commodity conversation page and a past prediction service attention tendency set as input through a tendency model weight index layer, outputting tendency model weight index information of each E-commerce commodity conversation page, and finally obtaining model weight index information of each E-commerce commodity conversation page through a fusion network layer according to decision precision model weight index information and tendency model weight index information which are combined with each E-commerce commodity conversation page.

On the basis, the network weight in the business concern tendency prediction layer can be adjusted through the hidden layer according to the model weight index information of each e-commerce commodity conversation page and the classification result information of the predicted business concern tendency, for example, the model weight index information of all e-commerce commodity conversation pages behind each e-commerce commodity conversation page is taken as input, the total model weight index information of each e-commerce commodity conversation page is obtained in a summing mode, and then the total model weight index information of each e-commerce commodity conversation page and the classification result information of the business concern tendency are subjected to weight updating through a gradient descent method. After artificial intelligence training is completed, the input layer and the business concern tendency prediction layer are reserved, and the trained business concern tendency prediction model can be obtained.

For example, based on the same inventive concept, the embodiments of the present disclosure provide a method for training a business concern tendency, including:

step A501, at least one annotation data set is obtained, wherein the annotation data set comprises session behavior characteristics and service attention tendency of each E-commerce commodity session page in a one-time historical session initiation process.

Step A502, inputting the conversation behavior characteristics of each e-commerce commodity conversation page in the marked data set to a prediction network layer of the business concern tendency prediction model to be trained, and obtaining classification result information of the business concern tendency of each e-commerce commodity conversation page output by the prediction network layer.

And step A503, screening according to the classification result information of the business concern tendency, and obtaining the predicted business concern tendency of each E-commerce commodity conversation page.

Step A504, inputting a model weight index layer according to the service attention tendency in the labeled data set and the past predicted service attention tendency set of each E-commerce commodity conversation page, and obtaining model weight index information of each E-commerce commodity conversation page output by the model weight index layer.

Step A505, according to the model weight index information of each E-commerce commodity conversation page and the classification result information of the predicted business concern tendency, performing weight updating on the network weight of the predicted network layer, and taking the trained predicted network layer as a target business concern tendency prediction model.

Fig. 3 is a schematic diagram of functional modules of an artificial intelligence based content recommendation apparatus 300 according to an embodiment of the disclosure, and the functions of the functional modules of the artificial intelligence based content recommendation apparatus 300 are described in detail below.

The mining module 310 is configured to obtain frequent operation item big data of the designated e-commerce service object according to a service attention tendency of the designated e-commerce service object on the current e-commerce commodity conversation page, and perform interest point mining on the frequent operation item big data through an interest point mining strategy to obtain interest point mining data of a conversation service process in the frequent operation item big data.

The analysis module 320 is configured to perform session topic distribution matrix analysis based on the interest point mining data of the session service flow to obtain a target session topic distribution matrix of the session service flow.

The analyzing module 330 is configured to perform weighted directed operation business graph analysis on the frequent operation item big data based on the machine learning model, so as to obtain a weighted directed operation business graph of the session service flow.

The extracting module 340 is configured to perform session interest path matching on the target session topic distribution matrix of the session service flow in the frequently-operated item big data and the weighted directed operation service graph of the session service flow to obtain session interest path matching information of the session service flow, and perform key interest object extraction on the frequently-operated item big data based on the session interest path matching information of the session service flow to obtain a key interest object set of the session service flow, where the key interest object set is used for e-commerce content recommendation.

Fig. 4 is a schematic diagram illustrating a hardware structure of an artificial intelligence content recommendation system 100 for implementing the artificial intelligence based content recommendation method, according to an embodiment of the present disclosure, and as shown in fig. 4, the artificial intelligence content recommendation system 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120, so that the processor 110 may execute the artificial intelligence based content recommendation method according to the above method embodiment, the processor 110, the machine-readable storage medium 120 and the communication unit 140 are connected through the bus 130, and the processor 110 may be configured to control transceiving actions of the communication unit 140, so as to perform data transceiving with the intelligent online service terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned various method embodiments executed by the artificial intelligence content recommendation system 100, which implement principles and technical effects are similar, and this embodiment is not described herein again.

In addition, the embodiment of the disclosure also provides a readable storage medium, in which a computer execution instruction is preset, and when a processor executes the computer execution instruction, the content recommendation method based on artificial intelligence is implemented.

Finally, it should be understood that the examples in this specification are only intended to illustrate the principles of the examples in this specification. Other variations are also possible within the scope of this description. Accordingly, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be seen as matching the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A content recommendation method based on artificial intelligence is characterized in that the method is applied to an artificial intelligence content recommendation system, the artificial intelligence content recommendation system is in communication connection with a plurality of intelligent online service terminals, and the method comprises the following steps:

2. The artificial intelligence based content recommendation method according to claim 1, wherein the performing interest point mining on frequently operated item big data through an interest point mining strategy to obtain interest point mining data of a session service flow in the frequently operated item big data comprises:

acquiring an operation behavior updating fragment sequence generated by the commodity conversation service of each frequent operation item data in the frequent operation item big data, wherein the operation behavior updating fragment sequence comprises an operation behavior updating fragment taking each e-commerce commodity label as an operation object, and the operation behavior updating fragment comprises operation triggering information and an operation triggering result of the e-commerce commodity label and an operation change log in the e-commerce commodity label;

for each E-commerce commodity label, according to each change unit log in a plurality of change unit logs in an operation change log of the E-commerce commodity label of each frequently-operated item data, determining whether each operation business record information in the change unit log is a key operation business record according to a record identifier of operation business record information in the change unit log, determining session interest information of each session service data packet corresponding to the change unit log according to a record category of the key operation business record in the change unit log, dividing the session interest information of each session service data packet into a plurality of sub-session interest information according to the session interest information of each operation business record information in each sub-session interest information and a preset interest source range, and determining whether the session interest information of each session service data packet is session interest information of a target preset legal interest source or not according to an interest source and a preset interest source range of each operation business record information in each sub-session interest information Wherein each operation service record information corresponds to each operation change behavior;

and obtaining interest point mining data of a session service process in the frequent operation item big data based on the determined session interest information of the target preset legal interest source.

3. The artificial intelligence based content recommendation method according to claim 2, wherein the step of obtaining interest point mining data of a session service flow in the frequently operated item big data based on the determined session interest information of the target preset legal interest source comprises:

obtaining interest description feature information of each operation service record information in session interest information of a preset interest description strategy matched with the target preset legal interest source, wherein the interest description feature information comprises interest description capture information and interest description application information, and the preset interest description strategy comprises description strategies corresponding to different interest description modes;

determining dynamic attribute information of dynamic scene attributes and static attribute information of each static scene attribute of each interest description according to the interest description feature information of each operation change log of different E-commerce commodity labels in the operation behavior update fragment sequence, determining an interest description label object of each frequent operation item data in the E-commerce commodity label according to the dynamic attribute information of the dynamic scene attribute and the static attribute information of each static scene attribute of each interest description in the conversation interest information of the target preset legal interest source, and taking the interest point information in the description range of the interest description label object and the interest point information in the description range of the interest description label object which is out of the description range of the interest description label object and is related to the interest description of the interest description label object as the interest point information of each frequent operation item data in the E-commerce commodity label, and after the interest point information of each frequently-operated item data in all E-commerce commodity labels is collected, obtaining interest point mining data of a session service process in the frequently-operated item big data.

4. The artificial intelligence based content recommendation method according to claim 1, wherein the step of performing session topic distribution matrix analysis based on the interest point mining data of the session service process to obtain a target session topic distribution matrix of the session service process comprises:

obtaining topic embedding vector reference information of a topic embedding vector sequence marked on associated topic information of each interest point information in the interest point mining data of the session service process, and determining a first topic participation behavior set corresponding to the topic embedding vector reference information, wherein the topic embedding vector reference information comprises embedding vector information of topic vector calculation relationship information determined according to topic vector input information and topic vector output information of the topic embedding vector sequence, and the first topic participation behavior set comprises the high-low order of a plurality of topic participation behavior priorities of the embedding vector information;

determining associated topic information of each interest point information based on a first topic vector component of topic vector input information and a second topic vector component of topic vector output information;

determining first analysis information for performing K-nearest neighbor algorithm analysis on the first topic participation behavior set according to the topic participation behavior priority relationship of the first topic vector component and the second topic vector component;

performing K nearest neighbor algorithm analysis on the first topic participation behavior set based on the first analysis information to obtain a second topic participation behavior set;

performing topic active category clustering on the second topic participation behavior set to obtain a plurality of topic active category clustering sets, and performing feature extraction on each topic active category clustering set to obtain topic active category clustering features;

determining a conversation topic distribution matrix of each interest point information according to a conversation topic distribution matrix corresponding to a plurality of topic active category clustering features corresponding to the second topic participation behavior set;

and obtaining a target conversation theme distribution matrix of the conversation service process based on the conversation theme distribution matrix of each interest point information.

5. The artificial intelligence based content recommendation method according to claim 1, wherein the step of performing weighted directed operation business graph analysis on the frequent operation item big data based on the machine learning model to obtain a weighted directed operation business graph of the session service flow comprises:

inputting the frequent operation item big data into a preset machine learning model to obtain the matching degree of the frequent operation item big data matched with each preset directed service node, wherein the preset machine learning model is configured with the corresponding relation between the compiling characteristics of different frequent operation item big data and the matching parameters of each preset directed service node;

determining a target directed service node corresponding to the frequent operation item big data according to the matching degree of the frequent operation item big data matched with each preset directed service node;

and extracting the weighted directed operation service graph matched with each session service flow from the directed service graph information of the target directed service node corresponding to the frequent operation item big data.

6. The artificial intelligence based content recommendation method according to any one of claims 1-5, wherein the step of performing session interest path matching between the target session topic distribution matrix of the session service flow in the frequently operated item big data and the weighted directed operation business graph of the session service flow to obtain the session interest path matching information of the session service flow comprises:

adding the target session theme distribution matrix and the weighted directed operation service graph of the session service flow into a session interest path matching network, and determining the target session theme distribution matrix and the path corresponding information of the weighted directed operation service graph of the session service flow corresponding to each preset session service path from the session interest path matching network;

clustering the information corresponding to each path according to an interest source of a path relation between preset session service paths in the information corresponding to each different path to obtain at least one path corresponding information cluster; the method comprises the steps that an interest source of a path relation of a preset session service path in any two pieces of path corresponding information in a same path corresponding information cluster covers a preset interest source range;

aiming at each path corresponding information cluster, determining unit path matching information corresponding to a target session theme distribution matrix and a weighted directed operation business graph of the path corresponding information cluster aiming at the session service process from the session interest path matching network based on each path corresponding information in the path corresponding information cluster; the unit path matching information at least comprises characteristic information data of each path corresponding information in the path corresponding information cluster aiming at a target session topic distribution matrix of the session service process and each topic jumping relation of the weighted directed operation service graph, and the unit path matching information is used for determining that a preset session service path in each path corresponding information in the path corresponding information cluster corresponds to the target session topic distribution matrix of the session service process and topic output information of the weighted directed operation service graph;

determining topic output information of a topic relation corresponding to a preset session service path in each path corresponding information in the path corresponding information cluster based on the unit path matching information, performing path matching on the topic output information of the topic relation to obtain path matching information, and obtaining session interest path matching information of the session service flow according to the path matching information.

7. The artificial intelligence based content recommendation method according to claim 6, wherein the step of determining subject output information of a subject relationship corresponding to a preset session service path in each path corresponding information in the path corresponding information cluster based on the unit path matching information comprises:

determining subject rule information and subject access categories of the unit path matching information, determining a plurality of subject access combinations according to a plurality of path objects in a historical path object set, performing subject potential extension feature mining according to subject parameters of the path objects in each subject access combination and the subject access categories in the subject rule information to obtain a plurality of subject potential extension feature mining results respectively corresponding to the plurality of subject access combinations, and taking access auxiliary parameters of the subject access combinations corresponding to each subject potential extension feature mining result as subject access auxiliary parameters of each subject potential extension feature mining result;

respectively obtaining topic path rules which determine information corresponding to each path corresponding to the topic access category in the topic rule information based on the topic potential extension feature mining results to obtain a plurality of topic path rules, integrating the topic path rules obtained based on the topic potential extension feature mining results according to topic access auxiliary parameters of the topic potential extension feature mining results to obtain a first topic path rule set, wherein the access auxiliary parameters between any two path objects in each topic access combination are the same, the access auxiliary parameters corresponding to different topic access combinations are different, and each topic potential extension feature mining result is used for determining common open topic information corresponding to a preset session service path in information corresponding to each path corresponding to the topic access category in any set service interval, the topic potential extension feature mining result is obtained by mining the topic potential extension feature according to the historical path object set;

according to the historical path object set and the common open theme information of a plurality of preset session service paths corresponding to the theme access category, obtaining the open theme results of the preset session service paths between the theme rule information and the theme access category, and taking the target nodes corresponding to the open theme results corresponding to the preset session service paths as a second theme path rule;

and comparing the theme path related information of the first theme path rule set and the second theme path rule, and determining the theme output information of the theme relationship corresponding to each preset session service path according to the theme path related information and the common open theme information of each preset session service path.

8. The artificial intelligence-based content recommendation method according to claim 7, wherein determining topic output information of the topic relationship corresponding to each preset session service path according to the topic path related information and the common open topic information of each preset session service path comprises:

determining invitation scene attribute information of the e-commerce interaction scene corresponding to each preset session service path based on the theme path related information, and determining session scene theme matching information of each preset session service path through a theme matching relationship of common open theme information of each preset session service path in corresponding path corresponding information;

and determining theme output information of the theme relation corresponding to each preset conversation service path based on the invitation scene attribute information and the conversation scene theme matching information.

9. The artificial intelligence based content recommendation method according to any one of claims 1-8, wherein the extracting key interest objects from the frequently operated item big data based on the session interest path matching information of the session service process to obtain a set of key interest objects of the session service process comprises:

acquiring key interest object extraction information of the session service flow under the big data of the frequent operation items;

acquiring a key interest object set under the key interest object extraction information and topic interest path information corresponding to each key interest object in the key interest object set;

and recommending the E-commerce content to the specified E-commerce service object based on the topic interest path information corresponding to each key interest object.

10. An artificial intelligence content recommendation system, characterized in that the artificial intelligence content recommendation system comprises a processor, a machine readable storage medium, and a communication unit, the machine readable storage medium, the communication unit and the processor are associated through a bus system, the communication unit is used for being connected with at least one intelligent online service terminal in a communication mode, the machine readable storage medium is used for storing computer instructions, and the processor is used for executing the computer instructions in the machine readable storage medium to execute the artificial intelligence based content recommendation method according to any one of claims 1 to 9.