CN109961327A

CN109961327A - Data processing method, device, electronic equipment and computer readable storage medium

Info

Publication number: CN109961327A
Application number: CN201910290708.8A
Authority: CN
Inventors: 姜谷雨
Original assignee: Shanghai Lazhasi Information Technology Co Ltd
Current assignee: Shanghai Lazhasi Information Technology Co Ltd
Priority date: 2019-04-11
Filing date: 2019-04-11
Publication date: 2019-07-02

Abstract

The embodiment of the present disclosure discloses a kind of data processing method, device, electronic equipment and computer readable storage medium, the data processing method includes: to obtain user's history behavioral data, wherein, the user's history behavioral data includes the first historical behavior data and the second historical behavior data；The user behavior score based on accumulated time is calculated according to the user's history behavioral data；Combined treatment is carried out after pre-processing for the first behavior score and the second behavior score, obtains user behavior score.The technical solution can effectively improve the accuracy of user behavior feature data, so as to provide better service for user, increase the chance for user service, improve the efficiency of service of internet, promote the service quality of internet.

Description

Data processing method and device, electronic equipment and computer readable storage medium

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a data processing method and apparatus, an electronic device, and a computer-readable storage medium.

Background

With the development of the internet technology, more and more merchants or service providers provide services for users through internet platforms, and the characteristics of the user behavior are obtained by insights, so that better services can be provided for the users, the opportunities for providing services for the users are increased, the service efficiency of the internet is improved, and the service quality of the internet is improved. However, in the prior art, the accuracy of research on the user behavior characteristics is low, and the requirements of merchants or service providers on improving the working efficiency and improving the service quality cannot be met.

Disclosure of Invention

The embodiment of the disclosure provides a data processing method and device, electronic equipment and a computer-readable storage medium.

In a first aspect, an embodiment of the present disclosure provides a data processing method.

Specifically, the data processing method includes:

acquiring historical behavior data of a user, wherein the historical behavior data of the user comprises first historical behavior data and second historical behavior data;

calculating a user behavior score based on time accumulation according to the user historical behavior data, wherein the user behavior score comprises a first behavior score and a second behavior score, the first behavior score is calculated according to the first historical behavior data, and the second behavior score is calculated according to the second historical behavior data;

and preprocessing the first behavior score and the second behavior score and then performing combined processing to obtain a user behavior score.

With reference to the first aspect, in a first implementation manner of the first aspect, the first historical behavior data is explicit data, and includes one or more of the following data: user transaction data, user collection data and user tagging data; and/or the presence of a gas in the gas,

the second historical behavior data is implicit data and comprises one or more of the following data: user click data, user search data, and user browse data.

With reference to the first aspect and the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the calculating a time-accumulation-based user behavior score according to the historical user behavior data includes:

determining a score initial value and a score increasing factor;

calculating the frequency of behavior objects according to the historical behavior data of the user;

and adding the product of the score incremental factor and the frequency to the initial score value, and calculating to obtain the user behavior score based on time accumulation.

With reference to the first aspect, the first implementation manner of the first aspect, and the second implementation manner of the first aspect, in a third implementation manner of the first aspect, when the behavior object includes one or more behavior factors, the calculating, according to the historical behavior data of the user, to obtain a time-accumulation-based user behavior score includes:

determining one or more behavior factors, and initial values of scores and incremental factors of scores corresponding to the behavior factors;

calculating the frequency of the behavior factors according to the historical behavior data of the user;

adding the product of the score incremental factor and the frequency to the initial score value, and calculating to obtain a score corresponding to the behavior factor;

and combining the scores corresponding to the behavior factors to obtain the user behavior score facing the behavior object or the behavior factors and accumulated based on time.

With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, and the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the obtaining a user behavior score by performing a combination process after performing a preprocessing on the first behavior score and the second behavior score includes:

preprocessing the first and second behavior scores;

determining a first combined weight for the first behavior score and a second combined weight for the second behavior score;

and according to the first combination weight and the second combination weight, carrying out weighted average on the first behavior score and the second behavior score to obtain a user behavior score.

With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, the third implementation manner of the first aspect, and the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the preprocessing the first behavior score and the second behavior score is implemented as:

acquiring a first minimum score and a first maximum score of scores corresponding to the behavioral factors in the first behavioral scores, and acquiring a second minimum score and a second maximum score of scores corresponding to the behavioral factors in the second behavioral scores;

calculating a first difference between the scores corresponding to the behavioral factors in the first behavioral scores and the first minimum score and a second difference between the scores corresponding to the behavioral factors in the first behavioral scores and the first minimum score, and calculating a third difference between the scores corresponding to the behavioral factors in the second behavioral scores and the second minimum score and a fourth difference between the scores corresponding to the second maximum score and the second minimum score;

and calculating a first quotient value between the first difference value and the second difference value, determining the first quotient value as a preprocessed first behavior score, calculating a second quotient value between the third difference value and the fourth difference value, and determining the second quotient value as a preprocessed second behavior score.

With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, the third implementation manner of the first aspect, the fourth implementation manner of the first aspect, and the fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect, the embodiment of the present invention further includes:

and executing preset operation according to the user behavior score.

In a second aspect, a data processing apparatus is provided in an embodiment of the present disclosure.

Specifically, the data processing apparatus includes:

the obtaining module is configured to obtain user historical behavior data, wherein the user historical behavior data comprises first historical behavior data and second historical behavior data;

a calculating module configured to calculate a user behavior score accumulated based on time according to the user historical behavior data, wherein the user behavior score comprises a first behavior score and a second behavior score, the first behavior score is calculated according to the first historical behavior data, and the second behavior score is calculated according to the second historical behavior data;

and the processing module is configured to carry out combined processing after the first behavior score and the second behavior score are preprocessed, so as to obtain a user behavior score.

With reference to the second aspect, in a first implementation manner of the second aspect, the first historical behavior data is explicit data, and includes one or more of the following data: user transaction data, user collection data and user tagging data; and/or the presence of a gas in the gas,

With reference to the second aspect and the first implementation manner of the second aspect, in a second implementation manner of the second aspect, the computing module includes:

a first determination submodule configured to determine a score initial value and a score increment factor;

a first calculation submodule configured to calculate the frequency of occurrence of a behavior object according to the user historical behavior data;

and the second calculation sub-module is configured to add the product of the score increment factor and the frequency to the initial score value, and calculate the user behavior score based on time accumulation.

With reference to the second aspect, the first implementation manner of the second aspect, and the second implementation manner of the second aspect, in a third implementation manner of the second aspect, in an embodiment of the present invention, when the behavior object includes one or more behavior factors, the calculation module includes:

the second determining submodule is configured to determine one or more behavior factors, and initial values of scores and incremental factors of scores corresponding to the behavior factors;

a third calculation submodule configured to calculate a frequency of occurrence of the behavioral factor from the user historical behavior data;

the fourth calculation sub-module is configured to add the product of the score incremental factor and the frequency to the initial score value, and calculate a score corresponding to the behavior factor;

and the combination sub-module is configured to combine the scores corresponding to the behavior factors to obtain a user behavior score facing the behavior object or the behavior factors and accumulated based on time.

With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, and the third implementation manner of the second aspect, in a fourth implementation manner of the second aspect, the embodiment of the present invention includes:

a pre-processing sub-module configured to pre-process the first and second behavior scores;

a third determination submodule configured to determine a first combined weight of the first behavior score and a second combined weight of the second behavior score;

and the weighted average submodule is configured to perform weighted average on the first behavior score and the second behavior score according to the first combination weight and the second combination weight to obtain a user behavior score.

With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, the third implementation manner of the second aspect, and the fourth implementation manner of the second aspect, in a fifth implementation manner of the second aspect, the preprocessing submodule is configured to:

With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, the third implementation manner of the second aspect, the fourth implementation manner of the second aspect, and the fifth implementation manner of the second aspect, in a sixth implementation manner of the second aspect, the embodiment of the present invention further includes:

and the execution module is configured to execute preset operation according to the user behavior score.

In a third aspect, the disclosed embodiments provide an electronic device, including a memory and a processor, where the memory is configured to store one or more computer instructions, where the one or more computer instructions are executed by the processor to implement the method steps of the data processing method in the first aspect.

In a fourth aspect, the disclosed embodiments provide a computer-readable storage medium for storing computer instructions for a data processing apparatus, which contains computer instructions for executing the data processing method in the first aspect to the data processing apparatus.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

according to the technical scheme, the user historical behavior data of different categories are obtained, the corresponding user behavior scores are obtained through calculation, and the accurate user behavior evaluation values can be obtained through preset combination processing of the obtained user behavior scores. According to the technical scheme, the accuracy of the user behavior characteristic data can be effectively improved, so that better service can be provided for the user, the opportunity of serving the user is increased, the service efficiency of the internet is improved, and the service quality of the internet is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:

FIG. 1 shows a flow diagram of a data processing method according to an embodiment of the present disclosure;

FIG. 2 shows a flow chart of step S102 of the data processing method according to the embodiment shown in FIG. 1;

FIG. 3 shows a flow chart of step S102 of a data processing method according to another embodiment shown in FIG. 1;

FIG. 4 shows a flow chart of step S103 of the data processing method according to the embodiment shown in FIG. 1;

FIG. 5 shows a flow diagram of a data processing method according to another embodiment of the present disclosure;

FIG. 6 shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure;

FIG. 7 shows a block diagram of a computing module 602 of the data processing apparatus according to the embodiment shown in FIG. 6;

FIG. 8 is a block diagram of a computing module 602 of the data processing apparatus according to another embodiment shown in FIG. 6;

fig. 9 shows a block diagram of a processing module 603 of the data processing apparatus according to the embodiment shown in fig. 6;

fig. 10 shows a block diagram of a data processing apparatus according to another embodiment of the present disclosure;

FIG. 11 shows a block diagram of an electronic device according to an embodiment of the present disclosure;

FIG. 12 is a schematic block diagram of a computer system suitable for use in implementing a data processing method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.

In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.

It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

According to the technical scheme provided by the embodiment of the disclosure, the user historical behavior data of different types are obtained, the corresponding user behavior scores are obtained through calculation, and the obtained user behavior scores are subjected to preset combination processing, so that the accurate user behavior evaluation value can be obtained. According to the technical scheme, the accuracy of the user behavior characteristic data can be effectively improved, so that better service can be provided for the user, the opportunity of serving the user is increased, the service efficiency of the internet is improved, and the service quality of the internet is improved.

Fig. 1 shows a flow diagram of a data processing method according to an embodiment of the present disclosure. As shown in fig. 1, the data processing method includes the following steps S101 to S103:

in step S101, obtaining user historical behavior data, where the user historical behavior data includes first historical behavior data and second historical behavior data;

in step S102, calculating a user behavior score accumulated based on time according to the user historical behavior data, where the user behavior score includes a first behavior score calculated according to the first historical behavior data and a second behavior score calculated according to the second historical behavior data;

in step S103, the first behavior score and the second behavior score are preprocessed and then combined to obtain a user behavior score.

As mentioned above, with the development of internet technology, more and more merchants or service providers provide services for users through internet platforms, and the characteristics of understanding user behaviors can be beneficial to providing better services for users, increasing opportunities for providing services for users, improving the service efficiency of the internet, and improving the service quality of the internet. However, in the prior art, the accuracy of research on the user behavior characteristics is low, and the requirements of merchants or service providers on improving the working efficiency and improving the service quality cannot be met.

In view of the above drawbacks, in this embodiment, a data processing method is provided, in which user historical behavior data of different categories are acquired, corresponding user behavior scores are obtained through calculation, and the obtained user behavior scores are subjected to a preset combination process, so that an accurate user behavior evaluation value can be obtained. According to the technical scheme, the accuracy of the user behavior characteristic data can be effectively improved, so that better service can be provided for the user, the opportunity of serving the user is increased, the service efficiency of the internet is improved, and the service quality of the internet is improved.

In an optional implementation manner of the embodiment, the user historical behavior data refers to behavior data of a certain user that can be acquired within a preset historical time, the behavioral data may include user transaction data, user collection data, user tagging data, user click data, user search data, user browsing data, and the like, wherein the user transaction data refers to data generated by a user performing ordering, purchasing, etc., and similarly, the user collection data refers to data generated by the user executing collection and other operations, the user tagging data refers to data generated by the user clicking favorite and executing star adding and other operations, the user click data refers to data generated by a user executing operations such as clicking, the user search data refers to data generated by the user executing operations such as searching and retrieving, and the user browsing data refers to data generated by the user executing operations such as browsing.

Wherein the behavior data may include user identification information (such as a user ID), behavior operation category information, behavior operation object information (such as a merchant ID), behavior operation sub-object information (such as a commodity ID), behavior operation price information, behavior occurrence time and the like for uniquely identifying the user identity, the specific content of the behavior data may differ according to the user behavior, for example, if the user a purchases a commodity with a price of 100 at the merchant a at 2019.1.1, the corresponding behavior data may be represented as { user ID, purchase, merchant ID, commodity ID, 100 yuan, 2019.1.1}, if the user a collects or marks the merchant a at 2019.1.1, the corresponding behavior data may be represented as { user ID, collection/mark, merchant ID, 2019.1.1}, if the merchant a clicks or browses the commodity a at 2019.1.1 or in the merchant a, then the corresponding behavior data may be represented as { user ID, click/browse, merchandise ID, merchant ID, 2019.1.1}, and if user a performed a search operation at 2019.1.1, then the corresponding behavior data may be represented as { user ID, search content, 2019.1.1 }.

The preset historical time can be set according to the requirements of practical application and the characteristics of user behavior data, and the specific value of the preset historical time is not particularly limited.

Considering that different user behaviors have different effects on acquiring behavior characteristics, for example, a ordering operation performed by the user a on the merchant a can better reflect the preference of the user a on the merchant a than a clicking operation performed by the merchant a. Therefore, in order to obtain more accurate user behavior feature information, the user historical behavior data needs to be divided into two different types of data for processing, and in an optional implementation manner of this embodiment, the user historical behavior data is divided into first historical behavior data embodied as explicit data and second historical behavior data embodied as implicit data, where the explicit data, that is, the first historical behavior data may include data capable of explicitly representing a user behavior feature, such as: user transaction data, user collection data, user tagging data, and the like; the implicit data, i.e. the second historical behavior data, may include data implicitly representing the behavior characteristics of the user, such as: user click data, user search data, and user browse data, among others. And subsequently, corresponding behavior scores can be respectively calculated according to the behavior data of different categories, and then the behavior scores are combined to obtain a final user behavior evaluation score.

In an optional implementation manner of this embodiment, the user behavior score is used to represent feature information of a user behavior, and specific content of the feature information is related to a behavior factor considered when calculating the user behavior score, for example, if the behavior factor is set to a user operation category of transaction, collection, tagging, clicking, searching, browsing, and the like, then the corresponding behavior score may represent an operation behavior characteristic of the user, and if the behavior factor is set to a commodity characteristic of a different merchant, then the corresponding behavior score may represent a selection preference characteristic of the user, and the like.

Considering that the user behavior data has a certain relation with the change of time, the user behavior data is richer along with the time, and the numerical value in the dimension of the behavior times is larger. Therefore, in an alternative implementation manner of the embodiment, when calculating the user behavior score, a time accumulation factor is added, that is, the user behavior score based on time accumulation refers to a user behavior score obtained by considering the time accumulation, and the time accumulation refers to a characteristic that the value of the calculation result or the intermediate calculation result increases with the passage of time.

In an alternative implementation manner of this embodiment, as shown in fig. 2, the step S102 of calculating a time-based cumulative user behavior score according to the user historical behavior data includes steps S201 to S203:

in step S201, an initial value of the score and a score increment factor are determined;

in step S202, calculating the frequency of behavior objects according to the historical behavior data of the user;

in step S203, the product of the score increment factor and the frequency is added to the initial score value, and a time-based cumulative user behavior score is calculated.

In order to fully reflect the characteristics of the user behavior, in this embodiment, the user behavior is subjected to cumulative statistics to obtain the user behavior score. Specifically, first, a score initial value and a score increasing factor are determined, where the score increasing factor is used to characterize a cumulative growth speed of user behavior, i.e. incremental stepping, and the score initial value and the score increasing factor can be flexibly set according to needs of practical applications, for example, the score initial value can be set to 0, and the score increasing factor can be set to 1; then, calculating the occurrence frequency of a behavior object according to the historical behavior data of the user, wherein the higher the occurrence frequency is, the higher the preference degree of the user for the behavior object is, wherein the behavior object can be an object such as a merchant, a service or a commodity; and finally, adding the product of the score incremental factor and the frequency to the initial score value to obtain the user behavior score based on time accumulation.

For example, if merchant a and merchant B are two different merchants, the initial value of the score is set to 0, and the incremental factor of the score is set to 1, then for the user behavior data: { user a, purchase, merchant a, 2019.2.1}, { user a, purchase, merchant a, 2019.2.8}, and { user a, purchase, merchant B, 2018.10.3}, the occurrence frequency of behavior object merchant a is 2 and the occurrence frequency of behavior object merchant B is 1 can be calculated, so the behavior score accumulated by user a based on time can be expressed as: { "Merchant A": 2, "Merchant B": 1 }.

In an optional implementation manner of this embodiment, when the behavior object includes one or more behavior factors, the score of the behavior object may be considered to be the score of the behavior factor included in the behavior object, and at this time, the scores corresponding to different behavior factors of the same behavior object may be combined to obtain the time-cumulative user behavior score for the behavior object, or the scores corresponding to the same behavior factor of different behavior objects may be combined to obtain the time-cumulative time-based user behavior score for the behavior object, or the scores corresponding to the same behavior factor of different behavior objects may be combined to obtain the time-based behavior score for the behavior factor And accumulated user behavior scores.

For example, if merchant a and merchant B are two different merchants that provide different tastes of meals, and it is known that merchant a can provide tastes, i.e. its corresponding behavior factors include: [ "taste Z", "taste X" ], the taste that merchant B can provide, i.e. its corresponding behavioral factors include: [ "taste M", "taste X" ], the initial values of the behavior factors are all set to 0, and the incremental factors of the scores are all set to 1, so for the user behavior data: { user a, purchase, merchant a, 2019.2.1}, { user a, purchase, merchant a, 2019.2.8}, and { user a, purchase, merchant B, 2018.10.3}, according to the user behavior score calculation method provided in the previous embodiment, the occurrence frequency of the behavior object merchant a is 2, the occurrence frequency of the behavior object merchant B is 1, and the behavior score of the user a facing the behavior object based on the time accumulation can be expressed as: { "Merchant A": 2, "Merchant B": 1}, then the user A behavior factor oriented time accumulation based user behavior score can be preliminarily expressed as: { 'taste Z': 2, 'taste X': 2, 'taste M': 1, 'taste X': 1}, and the final expression of the behavior score of the user based on time accumulation and oriented to the behavior factors can be obtained by combining the scores corresponding to the same behavior factors of different behavior objects: { 'taste Z': 2 ',' taste X ': 3', 'taste M': 1 }.

In another optional implementation manner of this embodiment, as shown in fig. 3, when the behavior object includes one or more behavior factors, the step S102 of calculating a time-based cumulative user behavior score according to the user historical behavior data includes steps S301 to S304:

in step S301, determining one or more behavior factors, initial score values corresponding to the behavior factors, and score increment factors;

in step S302, calculating the frequency of the behavior factors according to the historical behavior data of the user;

in step S303, adding the product of the score increment factor and the frequency to the initial score value, and calculating to obtain a score corresponding to the behavior factor;

in step S304, the scores corresponding to the behavior factors are combined to obtain a time-based cumulative user behavior score for the behavior object or the behavior factor.

In this implementation, when the behavior object includes one or more behavior factors, when calculating a user behavior score accumulated based on time according to the user historical behavior data, first determining the one or more behavior factors, a score initial value corresponding to the behavior factors, and a score increasing factor; then calculating the frequency of the behavior factors according to the historical behavior data of the user; then adding the product of the score incremental factor and the frequency to the initial score value, and calculating to obtain a score corresponding to the behavior factor; and finally, combining the scores corresponding to the behavior factors to obtain the user behavior score facing the behavior object or the behavior factors and based on time accumulation. Respectively calculating the corresponding scores of different behavior factors, and combining the scores into a behavior score vector facing the merchant or the behavior factors.

For example, if merchant a and merchant B are two different merchants that provide different tastes of meals, and it is known that merchant a can provide tastes, i.e. its corresponding behavior factors include: [ "taste Z", "taste X" ], the taste that merchant B can provide, i.e. its corresponding behavioral factors include: [ "taste M", "taste X" ], the initial values of the behavior factors are all set to 0, and the incremental factors of the scores are all set to 1, so for the user behavior data: { user a, purchase, merchant a, taste Z, 2019.2.1}, { user a, purchase, merchant a, taste X, 2019.2.8}, and { user a, purchase, merchant B, taste X, 2018.10.3}, the occurrence frequency of taste Z can be calculated as 1, and the occurrence frequency of taste X as 2, so the behavior factor-oriented user behavior score can be expressed as: { "taste Z": 1, "taste X": 2}, it is also possible to obtain the total occurrence frequency of the tastes provided by the merchant A as 2 and the total occurrence frequency of the tastes provided by the merchant B as 1 according to the needs of the practical application, and then the behavior score of the behavior object-oriented user can be expressed as: { "Merchant A": 2, "Merchant B": 1 }.

In an optional implementation manner of this embodiment, as shown in fig. 4, the step S103 of performing a combination process after preprocessing the first behavior score and the second behavior score to obtain the user behavior score includes steps S401 to S403:

in step S401, preprocessing the first behavior score and the second behavior score;

in step S402, a first combined weight of the first behavior score and a second combined weight of the second behavior score are determined;

in step S403, according to the first combination weight and the second combination weight, the first behavior score and the second behavior score are weighted and averaged to obtain a user behavior score.

In an alternative implementation of this embodiment, the first combining weight and the second combining weight may be set to be the same, for example, 0.5 each. Of course, if it is considered that the explicit data and the implicit data have different effects on the acquisition of the user behavior characteristics, in order to improve the accuracy of the user behavior characteristic data, in another optional implementation manner of this embodiment, the first combination weight may be set to be higher than the second combination weight.

In order to avoid the influence of abnormal values in the user behavior data and make the score data more standardized, in an optional implementation manner of the embodiment, the first behavior score and the second behavior score are subjected to a preprocessing operation before being combined, wherein the preprocessing operation may include one or more of the following operations: denoising, normalizing, etc.

In an optional implementation manner of this embodiment, the preprocessing is normalization, and in this implementation manner, specifically, the step S401 may be implemented as:

The above implementation can be represented by the following formula:

(x-x.min)/(x.max-x.min)*100，

wherein, x represents the score corresponding to the behavior factor in the behavior score, x.min represents the minimum score of the score corresponding to the behavior factor in the behavior score, and x.max represents the maximum score of the score corresponding to the behavior factor in the behavior score.

In consideration of the fact that the maximum score or the minimum score is distorted due to the existence of abnormal values in the user behavior data or due to abnormal operation of a part of the users, in an optional implementation manner of the embodiment, the maximum score x.max is taken as a score at a first preset position in scores corresponding to the behavior factors, and the minimum score x.min is taken as a score at a second preset position in scores corresponding to the behavior factors. For example, the scores corresponding to the behavioral factors may be sorted in descending order, and the value located at 95% of the place occupied is taken as the maximum score x.max, that is, in the descending order queue, the percentage of the value before the maximum score in the whole queue is 5%, and the value located at 5% of the place occupied is taken as the minimum score x.min, that is, in the descending order queue, the percentage of the value before the minimum score in the whole queue is 95%.

In an optional implementation manner of this embodiment, the method further includes a step of performing a preset operation according to the user behavior score, that is, as shown in fig. 5, the method includes the following steps S501 to S504:

in step S501, obtaining user historical behavior data, where the user historical behavior data includes first historical behavior data and second historical behavior data;

in step S502, calculating a user behavior score accumulated based on time according to the user historical behavior data, where the user behavior score includes a first behavior score calculated according to the first historical behavior data and a second behavior score calculated according to the second historical behavior data;

in step S503, the first behavior score and the second behavior score are preprocessed and then combined to obtain a user behavior score;

in step S504, a preset operation is performed according to the user behavior score.

In an optional implementation manner of this embodiment, the preset operation may include one or more of the following operations: merchant, service, taste, etc. recommendation operations, statistical operations, etc.

When the preset operation is a recommendation operation, the step S504, that is, the step of performing the preset operation according to the user behavior score, may be implemented as:

ranking the user behavior scores;

and executing recommendation operation according to the sorting result.

In an optional implementation manner of this embodiment, when the user behavior scores are sorted, when the user behavior scores are a single numerical value, the user behavior scores are directly sorted; when the user behavior scores comprise scores corresponding to different behavior factors, the scores corresponding to the different behavior factors can be sorted, and the sum of the scores of the behavior factors of different behavior objects can also be sorted.

When the sorting is carried out, the sorting can be carried out according to the sequence of scores from large to small and also according to the sequence of scores from small to large.

In an optional implementation manner of this embodiment, when the recommendation operation is performed according to the sorting result, the child object of the behavior factor with the highest score may be recommended to the user according to the sorting result, the behavior object related to the behavior factor with the highest score may also be recommended, and the behavior object with the highest score sum of the behavior factors may also be recommended. By way of example above, assume that merchant a is able to provide tastes, i.e., its corresponding behavioral factors include: [ "taste Z", "taste X" ], the taste that merchant B can provide, i.e. its corresponding behavioral factors include: [ "taste M", "taste X" ], the behavior score of the user a based on the behavior factors is: { "taste Z": 2, "taste X": 3, "taste M": 1}, then if the scores corresponding to different behavior factors are sorted, the sorting results of different behavior factors can be obtained: the taste X is more than the taste Z is more than the taste M, and at the moment, dishes with the taste X can be recommended to the user A, and merchants capable of providing products with the taste X can also be recommended to the user A; if the sum of the behavior factor scores of different behavior objects is sorted, the sorting result of the behavior objects is obtained: and the merchant A is larger than the merchant B, and at the moment, the merchant A with a higher total score can be recommended to the user A.

The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.

Fig. 6 shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of both. As shown in fig. 6, the data processing apparatus includes:

an obtaining module 601 configured to obtain user historical behavior data, where the user historical behavior data includes first historical behavior data and second historical behavior data;

a calculating module 602 configured to calculate a time-based cumulative user behavior score according to the user historical behavior data, wherein the user behavior score includes a first behavior score calculated according to the first historical behavior data and a second behavior score calculated according to the second historical behavior data;

the processing module 603 is configured to perform preprocessing on the first behavior score and the second behavior score and then perform combination processing to obtain a user behavior score.

In view of the above drawbacks, in this embodiment, a data processing apparatus is provided, which obtains different types of user historical behavior data, calculates a corresponding user behavior score, and performs a preset combination process on the obtained user behavior score to obtain an accurate user behavior evaluation value. According to the technical scheme, the accuracy of the user behavior characteristic data can be effectively improved, so that better service can be provided for the user, the opportunity of serving the user is increased, the service efficiency of the internet is improved, and the service quality of the internet is improved.

In an optional implementation manner of this embodiment, as shown in fig. 7, the calculating module 602 includes:

a first determining sub-module 701 configured to determine a score initial value and a score increasing factor;

a first calculating submodule 702 configured to calculate the frequency of occurrence of a behavior object according to the user historical behavior data;

a second calculating sub-module 703 configured to add the product of the score increasing factor and the frequency to the initial score value to calculate a time-accumulation-based user behavior score.

As mentioned above, the user behavior data has a certain relationship with the change of time, and as time goes on, the user behavior data is richer, and the value in the behavior time dimension is larger, so that in order to fully reflect the characteristics of the user behavior, in this embodiment, the calculation module 602 performs cumulative statistics on the user behavior to obtain the user behavior score, in consideration of that the same user may generate multiple behaviors. Specifically, the first determining sub-module 701 determines a first score value and a score increasing factor, where the score increasing factor is used to characterize a cumulative growth rate of user behavior, that is, an incremental step, and the first score value and the score increasing factor may be flexibly set according to the needs of practical applications, for example, the first score value may be set to 0 and the score increasing factor may be set to 1; the first calculating submodule 702 calculates the occurrence frequency of a behavior object according to the historical behavior data of the user, wherein the higher the occurrence frequency is, the higher the preference degree of the user for the behavior object is, wherein the behavior object can be an object such as a merchant, a service or a commodity; the second calculation sub-module 703 adds the product of the score increment factor and the frequency to the initial score value, so as to calculate the time-based cumulative user behavior score.

In another optional implementation manner of this embodiment, as shown in fig. 8, when the behavior object includes one or more behavior factors, the calculating module 602 includes:

a second determining sub-module 801 configured to determine one or more behavior factors, initial values of scores corresponding to the behavior factors, and score increasing factors;

a third computing submodule 802 configured to compute a frequency of occurrence of the behavioral factor from the user historical behavior data;

a fourth calculating sub-module 803, configured to add the product of the score increment factor and the frequency to the initial score value, and calculate a score corresponding to the behavior factor;

and the combining sub-module 804 is configured to combine the scores corresponding to the behavior factors to obtain a user behavior score facing the behavior object or the behavior factors and accumulated based on time.

In this implementation, when the behavior object includes one or more behavior factors, when calculating a time-based cumulative user behavior score according to the user historical behavior data, the second determining sub-module 801 first determines the one or more behavior factors, a score initial value corresponding to the behavior factors, and a score increasing factor; the third computation submodule 802 computes the frequency of the behavior factor according to the historical behavior data of the user; the fourth calculation sub-module 803 adds the product of the score incremental factor and the frequency to the initial score value to calculate a score corresponding to the behavior factor; the combination sub-module 804 combines the scores corresponding to the behavior factors to obtain a user behavior score based on time accumulation and oriented to the behavior object or the behavior factors. Respectively calculating the corresponding scores of different behavior factors, and combining the scores into a behavior score vector facing the merchant or the behavior factors.

In an optional implementation manner of this embodiment, as shown in fig. 9, the processing module 603 includes:

a preprocessing submodule 901 configured to preprocess the first behavior score and the second behavior score;

a third determining submodule 902 configured to determine a first combined weight of the first behavior score and a second combined weight of the second behavior score;

a weighted average sub-module 903 configured to perform weighted average on the first behavior score and the second behavior score according to the first combination weight and the second combination weight, so as to obtain a user behavior score.

In order to avoid the influence of abnormal values in the user behavior data and make the score data more standardized, in an optional implementation manner of the present embodiment, the preprocessing sub-module 901 is configured to perform a preprocessing operation on the first behavior score and the second behavior score before combining them, where the preprocessing may include one or more of the following operations: denoising, normalizing, etc.

In an optional implementation manner of this embodiment, the preprocessing is normalization, and in this implementation manner, specifically, the preprocessing submodule 901 may be configured to:

The above implementation can be represented by the following formula:

(x-x.min)/(x.max-x.min)*100，

In an optional implementation manner of this embodiment, the apparatus further includes a part that performs a preset operation according to the user behavior score, that is, as shown in fig. 10, the apparatus includes:

an obtaining module 1001 configured to obtain user historical behavior data, where the user historical behavior data includes first historical behavior data and second historical behavior data;

a calculating module 1002 configured to calculate a time-based cumulative user behavior score according to the user historical behavior data, wherein the user behavior score includes a first behavior score calculated according to the first historical behavior data and a second behavior score calculated according to the second historical behavior data;

the processing module 1003 is configured to perform combination processing after preprocessing the first behavior score and the second behavior score to obtain a user behavior score;

an executing module 1004 configured to execute a preset operation according to the user behavior score.

When the preset operation is a recommendation operation, the executing module 1004 may be configured to:

ranking the user behavior scores;

and executing recommendation operation according to the sorting result.

In an optional implementation manner of this embodiment, when the execution module 1004 ranks the user behavior scores, when the user behavior scores are a single numerical value, the user behavior scores are directly ranked; when the user behavior scores comprise scores corresponding to different behavior factors, the scores corresponding to the different behavior factors can be sorted, and the sum of the scores of the behavior factors of different behavior objects can also be sorted.

When the execution module 1004 performs sorting, the sorting may be performed according to a sequence of scores from large to small, or according to a sequence of scores from small to large.

In an optional implementation manner of this embodiment, when the executing module 1004 executes the recommending operation according to the sorting result, the sub-object of the behavior factor with the highest score may be recommended to the user according to the sorting result, the behavior object related to the behavior factor with the highest score may also be recommended, and the behavior object with the highest score sum of the behavior factors may also be recommended. By way of example above, assume that merchant a is able to provide tastes, i.e., its corresponding behavioral factors include: [ "taste Z", "taste X" ], the taste that merchant B can provide, i.e. its corresponding behavioral factors include: [ "taste M", "taste X" ], the behavior score of the user a based on the behavior factors is: { "taste Z": 2, "taste X": 3, "taste M": 1}, then if the scores corresponding to different behavior factors are sorted, the sorting results of different behavior factors can be obtained: the taste X is more than the taste Z is more than the taste M, and at the moment, dishes with the taste X can be recommended to the user A, and merchants capable of providing products with the taste X can also be recommended to the user A; if the sum of the behavior factor scores of different behavior objects is sorted, the sorting result of the behavior objects is obtained: and the merchant A is larger than the merchant B, and at the moment, the merchant A with a higher total score can be recommended to the user A.

The present disclosure also discloses an electronic device, fig. 11 shows a block diagram of an electronic device according to an embodiment of the present disclosure, and as shown in fig. 11, the electronic device 1100 includes a memory 1101 and a processor 1102; wherein,

the memory 1101 is used to store one or more computer instructions that are executed by the processor 1102 to implement the above-described method steps.

As shown in fig. 12, the computer system 1200 includes a Central Processing Unit (CPU)1201, which can execute various processes in the above-described embodiments according to a program stored in a Read Only Memory (ROM)1202 or a program loaded from a storage section 1208 into a Random Access Memory (RAM) 1203. In the RAM1203, various programs and data necessary for the operation of the system 1200 are also stored. The CPU1201, ROM1202, and RAM1203 are connected to each other by a bus 1204. An input/output (I/O) interface 1205 is also connected to bus 1204.

The following components are connected to the I/O interface 1205: an input section 1206 including a keyboard, a mouse, and the like; an output portion 1207 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 1208 including a hard disk and the like; and a communication section 1209 including a network interface card such as a LAN card, a modem, or the like. The communication section 1209 performs communication processing via a network such as the internet. A driver 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 1210 as necessary, so that a computer program read out therefrom is mounted into the storage section 1208 as necessary.

In particular, the above described methods may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for performing the above-described data processing method. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 1209, and/or installed from the removable medium 1211.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.

As another aspect, the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Claims

1. A data processing method, comprising:

2. The method of claim 1, wherein the first historical behavior data is explicit data comprising one or more of: user transaction data, user collection data and user tagging data; and/or the presence of a gas in the gas,

3. The method according to claim 1 or 2, wherein calculating a time-based cumulative user behavior score according to the user historical behavior data comprises:

determining a score initial value and a score increasing factor;

4. The method according to claim 1 or 2, wherein when the behavior object includes one or more behavior factors, the calculating a time-based cumulative user behavior score according to the user historical behavior data includes:

5. A data processing apparatus, comprising:

6. The apparatus of claim 5, wherein the first historical behavior data is explicit data comprising one or more of: user transaction data, user collection data and user tagging data; and/or the presence of a gas in the gas,

7. The apparatus of claim 5 or 6, wherein the computing module comprises:

8. The apparatus of claim 5 or 6, wherein when the behavior object comprises one or more behavior factors, the computing module comprises:

9. An electronic device comprising a memory and a processor; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps of any of claims 1-4.

10. A computer-readable storage medium having stored thereon computer instructions, characterized in that the computer instructions, when executed by a processor, carry out the method steps of any of claims 1-4.