CN113763134B - Information recommendation method, system, equipment and storage medium - Google Patents
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Abstract
The invention provides an information recommendation method, an information recommendation system, an information recommendation device and a storage medium, wherein the information recommendation method comprises the following steps: acquiring commodity information of an alternative commodity, determining an associated commodity of the alternative commodity, and acquiring commodity information of the associated commodity; selecting a recommended commodity from the candidate commodity according to commodity information of the candidate commodity and commodity information of the related commodity to obtain commodity information of the recommended commodity; performing display rule verification on the recommended commodity; if the recommended commodity passes the verification of the display rule, displaying commodity information of the recommended commodity; and if the recommended commodity does not pass the verification of the display rule, acquiring commodity information of the related commodity of the recommended commodity and displaying the commodity information. The invention maintains the logical reasonable consistency of the whole flow by introducing common intermediate commodities during information recommendation and information display.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an information recommendation method, system, device, and storage medium.
Background
In the prior art, two independent links are recommended and displayed, and the main interactive data are types and IDs. The recommended factors are relatively independent of, or otherwise do not affect, the elements presented. For commodities with thin characteristics, the recommendation layer is usually associated with other commodities with rich characteristics as compensation commodities so as to acquire more calculation factors; for low-inventory commodities with high real-time performance, a bottom protection logic is generally arranged on a display layer to cope with inventory change. Therefore, when the commodity with the characteristics of rarefaction and high real-time performance is provided, the compensation of the current recommendation layer and the protection of the display layer are independent, so that the logic inconsistency in the whole flow can be caused, and the display result of the final client is unreasonable.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide an information recommending method, an information recommending system, information recommending equipment and an information recommending storage medium, which keep the logic of the whole process reasonably consistent by introducing common intermediate commodities during information recommending and information displaying.
The embodiment of the invention provides an information recommendation method, which comprises the following steps:
Acquiring commodity information of an alternative commodity, determining an associated commodity of the alternative commodity, and acquiring commodity information of the associated commodity;
selecting a recommended commodity from the candidate commodity according to commodity information of the candidate commodity and commodity information of the related commodity to obtain commodity information of the recommended commodity;
performing display rule verification on the recommended commodity;
if the recommended commodity passes the verification of the display rule, displaying commodity information of the recommended commodity;
And if the recommended commodity does not pass the verification of the display rule, acquiring commodity information of the related commodity of the recommended commodity and displaying the commodity information.
In some embodiments, selecting a recommended commodity from the candidate commodity according to commodity information of the candidate commodity and commodity information of the associated commodity, including the steps of:
Determining characteristic data of the alternative commodity according to commodity information of the alternative commodity and commodity information of the related commodity;
Calculating the recommendation degree score of the candidate commodity by adopting a preset recommendation degree scoring rule according to the characteristic data of the candidate commodity;
And selecting recommended commodities from the candidate commodities according to the recommendation degree scores.
In some embodiments, selecting a recommended commodity from the candidate commodity according to commodity information of the candidate commodity and commodity information of the associated commodity, including the steps of:
Determining characteristic data of the alternative commodity according to commodity information of the alternative commodity and commodity information of the related commodity;
Inputting the feature vector of the candidate commodity into a trained recommended commodity selection model;
And selecting a recommended commodity from the candidate commodities according to the output data of the recommended commodity selection model.
In some embodiments, after determining the characteristic data of the candidate commodity, the method further comprises the steps of:
Calculating the contribution value of each associated commodity to the characteristic data of the alternative commodity;
and ordering the associated commodities according to the contribution value from large to small.
In some embodiments, after the recommended merchandise is validated by the presentation rule, if the recommended merchandise is not validated by the presentation rule, the following steps are performed:
sequentially selecting an associated commodity from front to back according to the contribution value sequence of the associated commodity;
Judging whether the selected associated commodity can pass through the verification of the display rule, if so, displaying commodity information of the selected associated commodity, otherwise, continuing to select the next associated commodity, and then executing the current step in a circulating way.
In some embodiments, calculating the contribution value of each associated commodity to the characteristic data of the candidate commodity includes the steps of:
determining the characteristic category corresponding to each associated commodity in the characteristic data of each candidate commodity to be used as the contribution characteristic category of each associated commodity;
for each associated commodity, the number of the contribution characteristic categories is taken as the contribution value of the associated commodity.
In some embodiments, calculating the contribution value of each associated commodity to the characteristic data of the candidate commodity includes the steps of:
determining the characteristic category corresponding to each associated commodity in the characteristic data of each candidate commodity to be used as the contribution characteristic category of each associated commodity;
and summing the feature weights corresponding to the corresponding contribution feature categories of the associated commodities to obtain the contribution values of the associated commodities.
In some embodiments, determining the associated merchandise of the alternative merchandise includes the steps of:
calculating the similarity of the alternative commodity and other commodities;
And taking other commodities with the similarity larger than a preset similarity threshold value with the alternative commodity or other commodities with the highest similarity with the alternative commodity as related commodities of the alternative commodity.
In some embodiments, the verifying the display rule for the recommended merchandise includes the steps of:
inquiring the stock of the recommended commodity, and judging whether the quantity of the stock of the recommended commodity meets the requirement of display rule verification.
The embodiment of the invention also provides an information recommendation system for realizing the information recommendation method, which comprises the following steps:
The information acquisition module is used for acquiring commodity information of the alternative commodity, determining an associated commodity of the alternative commodity and acquiring commodity information of the associated commodity;
the recommendation selection module is used for selecting recommended commodities from the candidate commodities according to commodity information of the candidate commodities and commodity information of the associated commodities to obtain commodity information of the recommended commodities;
The rule verification module is used for carrying out display rule verification on the recommended commodity;
The information display module displays commodity information of the recommended commodity if the recommended commodity passes the verification of the display rule; and if the recommended commodity does not pass the verification of the display rule, the information display module acquires commodity information of the related commodity of the recommended commodity and displays the commodity information.
The embodiment of the invention also provides information recommending equipment, which comprises the following steps:
A processor;
a memory having stored therein executable instructions of the processor;
Wherein the processor is configured to perform the steps of the information recommendation method via execution of the executable instructions.
The embodiment of the invention also provides a computer readable storage medium for storing a program which, when executed by a processor, implements the steps of the information recommendation method.
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.
The information recommendation method, system, equipment and storage medium have the following beneficial effects:
In the invention, the related commodity serving as the intermediate commodity is introduced in the process of information recommendation selection, and the related commodity is selected to be displayed as the bottom-protected commodity when the recommended commodity cannot pass the verification of the display rule in the process of information display, and the common intermediate commodity is introduced in the process of information recommendation and information display, so that the logic reasonable consistency of the whole process is maintained.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings.
FIG. 1 is a flow chart of an information recommendation method according to an embodiment of the present invention;
FIG. 2 is a flow chart of selecting recommended items from among alternative items according to an embodiment of the invention;
FIG. 3 is a flow chart of a presentation rule verification for recommended goods according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an information recommendation system according to an embodiment of the present invention;
FIG. 5 is a schematic diagram showing the structure of an information recommendation apparatus according to an embodiment of the present invention;
Fig. 6 is a schematic structural view of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only and not necessarily all steps are included. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
As shown in fig. 1, an embodiment of the present invention provides an information recommendation method, including the following steps:
s100: acquiring commodity information of an alternative commodity, determining an associated commodity of the alternative commodity, and acquiring commodity information of the associated commodity;
s200: selecting a recommended commodity from the candidate commodity according to commodity information of the candidate commodity and commodity information of the related commodity to obtain commodity information of the recommended commodity;
s300: performing display rule verification on the recommended commodity;
S400: if the recommended commodity passes the verification of the display rule, displaying commodity information of the recommended commodity;
s500: and if the recommended commodity does not pass the verification of the display rule, acquiring commodity information of the related commodity of the recommended commodity and displaying the commodity information.
According to the invention, in the process of information recommendation selection, the related commodity serving as the intermediate commodity is introduced in the process of information recommendation selection through the steps S100 and S200, in the process of information display, firstly, the display rule verification of the recommended commodity is carried out through the step S300, when the recommended commodity passes the display rule verification, the commodity information of the recommended commodity is directly displayed through the step S400, when the recommended commodity cannot pass the display rule verification, the related commodity is selected to be displayed as the bottom protection commodity through the step S500, and the common intermediate commodity is introduced in the process of information recommendation and information display, so that the logic reasonable consistency of the whole flow is maintained.
In this embodiment, the step S100: determining the associated commodity of the alternative commodity, comprising the following steps:
Calculating the similarity between the candidate commodity and other commodities, wherein the similarity between the two commodities is calculated, for example, the cosine similarity, euclidean distance and the like of the feature vectors of the two commodities are calculated, or the number of the same feature categories of the two commodities is used as the similarity and the like; for example, 80 feature categories are preset in total, feature values of the candidate commodity corresponding to each feature category are obtained, other commodity is selected, feature values of the candidate commodity corresponding to each feature category are obtained, if one commodity does not have data corresponding to one feature category, the feature values are set as default values, then feature vectors are obtained by combining the feature values of each feature category of the candidate commodity, feature vectors are obtained by combining the feature values of each feature category of the other commodity, cosine similarity, euclidean distance and the like of the two feature vectors are calculated, and the cosine similarity, euclidean distance and the like of the two feature vectors are used as the similarity of the two commodities; or, counting the number of feature categories of the candidate commodity and one other commodity, wherein the feature categories have the same feature value, for example, the feature value of fifteen feature categories of the candidate commodity is the same as the feature value of the feature category of one other commodity, and the similarity between the other commodity and the candidate commodity is 15 or 15/80;
And taking other commodities with the similarity larger than a preset similarity threshold value with the alternative commodity or other commodities with the highest similarity with the alternative commodity as related commodities of the alternative commodity.
For example, the invention can be used for recommending and displaying hotel group purchases/second killing goods. The hotel group purchase/second commodity killing is a typical commodity with thin characteristics and high real-time performance because of small inventory and short online time, and each independent commodity is difficult to accumulate enough factors which can be used as a recommendation calculation factor. When the characteristics of the group purchase/second commodity killing are insufficient, the recommended flow uses the hotel commodity related to the commodity, or the group purchase/second commodity killing of the same hotel in the past or the same star class as the house in the past or the same time as the house as the calculating factor of the compensation obtained by the related commodity.
As shown in fig. 2, in this embodiment, the step S200: selecting recommended commodities from the candidate commodities according to commodity information of the candidate commodities and commodity information of the associated commodities, wherein the method comprises the following steps:
S210: determining characteristic data of the alternative commodity according to commodity information of the alternative commodity and commodity information of the related commodity;
specifically, step S210 includes the steps of:
s211: extracting characteristic values of each characteristic category from commodity information of the candidate commodity;
S212: extracting the characteristic value of the characteristic category from the commodity information of the related commodity for the characteristic category which does not comprise the characteristic value in the candidate commodity;
s213: combining the characteristic value extracted from the commodity information of the candidate commodity with the characteristic value extracted from the commodity information of the related commodity to obtain the characteristic data of the candidate commodity;
This feature class, i.e. the calculation factor corresponding to the subsequent calculation score, may for example comprise geographical location, price, star rating, area, whether breakfast is involved, etc.;
S220: calculating the recommendation degree score of the candidate commodity by adopting a preset recommendation degree scoring rule according to the characteristic data of the candidate commodity;
The preset recommendation degree scoring rule may be, for example, a preset calculation formula, wherein the feature values of each feature class are used as variables, and for an alternative commodity, each feature value in the feature data is filled into the calculation formula to obtain a corresponding recommendation degree score;
For example, the calculation formula may be a weighted sum formula, that is, a weight is set for each feature class, and the feature value of each feature class is multiplied by the corresponding weight and then summed, so as to obtain the recommendation degree score. In calculating the recommendation degree score, the recommendation degree score can be calculated by further combining historical behavior data of different users. For example, a feature value of each feature class corresponding to the user is calculated from historical behavior data (purchase data, browse data, click data, etc.) of the user;
s230: and selecting recommended commodities from the candidate commodities according to the recommendation degree scores.
For example, the candidate commodities may be sequentially ranked according to the recommendation degree score from high to low, and the preset commodity recommendation number or the candidate commodities with the recommendation degree score greater than the preset score threshold value are selected from front to back to be used as the recommended commodities.
In another alternative embodiment, the step S200: selecting recommended commodities from the candidate commodities according to commodity information of the candidate commodities and commodity information of the associated commodities, wherein the method comprises the following steps:
Determining characteristic data of the alternative commodity according to commodity information of the alternative commodity and commodity information of the related commodity;
Inputting the feature vector of the candidate commodity into a trained recommended commodity selection model;
And selecting a recommended commodity from the candidate commodities according to the output data of the recommended commodity selection model.
The recommended commodity selection model may be a machine learning model such as a convolutional neural network, for example. And inputting the feature vector of the candidate commodity into the model, outputting the recommended probability of the commodity by the model, and taking the output probability of the model as a recommendation degree score. In calculating the recommendation score by the model, the user feature data may be further combined, for example, the user feature data and the feature data of the candidate commodity may be input into a two-input machine learning model, which outputs the matching probability of the user feature data and the feature data of the candidate commodity, and the matching probability may be used as the recommendation score.
As shown in fig. 3, in this embodiment, the step S200: after determining the characteristic data of the candidate commodity, the method further comprises the following steps:
s240: calculating the contribution value of each associated commodity to the characteristic data of the alternative commodity;
s250: and ordering the associated commodities according to the contribution value from large to small.
As shown in fig. 3, in this embodiment, the step S300: after the recommended commodity is subjected to the display rule verification, if the recommended commodity does not pass the display rule verification, executing the following steps:
S310: sequentially selecting an associated commodity from front to back according to the contribution value sequence of the associated commodity;
s320: judging whether the selected associated commodity can pass through the verification of the display rule;
if so, then proceed to step S400: displaying commodity information of the selected associated commodity;
Otherwise, the step S330 is continued: and continuing to select the next associated commodity according to the contribution value sequence of the associated commodity, and then continuing to step S320.
In this embodiment, the step S240: calculating the contribution value of each associated commodity to the characteristic data of the candidate commodity, wherein the contribution value comprises the following steps:
determining the characteristic category corresponding to each associated commodity in the characteristic data of each candidate commodity to be used as the contribution characteristic category of each associated commodity;
for example, there are 80 feature categories in total, the candidate commodity has only feature values of thirty feature categories, and ten feature values of ten feature categories are provided in one associated commodity, so that the contribution feature categories of the associated commodity are ten;
for each associated commodity, the number of the contribution feature categories is taken as the contribution value of the associated commodity, for example, ten contribution feature categories of one associated commodity, and the contribution value of the associated commodity is 10.
In another embodiment, the step S240: calculating the contribution value of each associated commodity to the characteristic data of the candidate commodity, wherein the contribution value comprises the following steps:
determining the characteristic category corresponding to each associated commodity in the characteristic data of each candidate commodity to be used as the contribution characteristic category of each associated commodity;
And summing the feature weights corresponding to the corresponding contribution feature categories of the associated commodities to obtain the contribution values of the associated commodities. That is, when calculating the contribution value, not only the number of contribution feature categories of each related commodity is considered, but also the weight values of different feature categories are considered, and the weight value of the important feature category is higher.
In this embodiment, the step S300: and carrying out display rule verification on the recommended commodity, wherein the verification of the real-time performance of the recommended commodity comprises that the stock quantity of the recommended commodity can meet the requirement of the display rule verification. Specifically, the step S300 includes the steps of:
inquiring the stock of the recommended commodity, and judging whether the quantity of the stock of the recommended commodity meets the requirement of display rule verification.
Therefore, the recommendation compensation data interaction is added between the recommendation layer and the display layer, and the protection bottom of the display layer is determined based on the data of the current recommendation compensation. And aiming at a commodity with a thin characteristic, the recommendation layer acquires a/a group of related commodities with the greatest comprehensive influence on a final result when acquiring a compensation calculation factor through related commodities, and includes the related commodities into recommendation data for issuing. The display layer performs normal validity check on the commodity, and the higher probability of the display layer can enter the display bottom protection logic due to low commodity stock and high real-time requirement. When entering the bottom protection logic, the display layer reads the associated commodity data in the recommendation issuing data, and uses the associated commodity which is recommended to be compensated at the present time as the bottom protection display commodity. Thereby realizing the logical consistency of recommendation and presentation.
The above hotel group purchase/second commodity killing scenario is illustrated as an example. After the recommended group purchase/second commodity killing is selected in step S200, not only the commodity information of the group purchase/second commodity killing is issued, but also the commodity information of the related commodity is issued along with the group purchase/second commodity killing, and in the issued related commodity list, the related commodities are ordered according to the size of the contribution value, namely, the degree of influence of the related commodity on the recommendation degree score.
And after entering the display layer, if the group purchase/second killing commodity does not pass the real-time verification and is judged to be invalid, entering the displayed bottom protection logic. In the bottom-protecting logic of the commodity, the display layer sequentially performs validity check according to the associated commodity list issued by the recommendation layer, and the first associated commodity passing the validity check is used as bottom-protecting display, so that the consistency and the interpretation of the display logic and the recommendation logic are kept.
As shown in fig. 4, an embodiment of the present invention further provides an information recommendation system, configured to implement the information recommendation method, where the system includes:
The information acquisition module M100 is used for acquiring commodity information of the alternative commodity, determining an associated commodity of the alternative commodity and acquiring commodity information of the associated commodity;
The recommendation selecting module M200 is configured to select a recommended commodity from the candidate commodities according to commodity information of the candidate commodity and commodity information of the associated commodity, so as to obtain commodity information of the recommended commodity;
the rule verification module M300 is used for carrying out display rule verification on the recommended commodity;
The information display module M400 displays commodity information of the recommended commodity if the recommended commodity passes the verification of the display rule; and if the recommended commodity does not pass the verification of the display rule, the information display module acquires commodity information of the related commodity of the recommended commodity and displays the commodity information.
According to the invention, the information acquisition module M100 and the recommendation selection module M200 are used for introducing the related commodity serving as the intermediate commodity in the process of information recommendation selection, in the process of information display, the rule verification module M300 is used for verifying the display rule of the recommended commodity, the information display module M400 is used for directly displaying commodity information of the recommended commodity when the recommended commodity passes the display rule verification, the information display module M400 is used for selecting the related commodity as the bottom protection commodity for display when the recommended commodity cannot pass the display rule verification, and the common intermediate commodity is introduced during information recommendation and information display, so that the logic reasonable consistency of the whole flow is maintained.
In the information recommendation system of the present invention, the functions of each module may be implemented by adopting the specific implementation manner of each step in the information recommendation method, for example, the information acquisition module M100 may acquire related information by adopting the specific implementation manner of step S100, the recommendation selection module M200 may select recommended goods by adopting the specific implementation manner of step S200, the rule verification module M300 may perform display rule verification by adopting the specific implementation manner of step S300, and the information display module M400 may perform goods display by adopting the specific implementation manner of step S400, which is not repeated here.
The embodiment of the invention also provides information recommending equipment, which comprises a processor; a memory having stored therein executable instructions of the processor; wherein the processor is configured to perform the steps of the information recommendation method via execution of the executable instructions.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" platform.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 5. The electronic device 600 shown in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 5, the electronic device 600 is embodied in the form of a general purpose computing device. Components of electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different system components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code executable by the processing unit 610 such that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above-mentioned information recommendation method section of the present specification. For example, the processing unit 610 may perform the steps as shown in fig. 1.
The memory unit 620 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
In the information recommendation device, the steps of the information recommendation method are realized when the program in the memory is executed by the processor, so that the device can obtain the technical effects of the information recommendation method.
The embodiment of the invention also provides a computer readable storage medium for storing a program which, when executed by a processor, implements the steps of the information recommendation method. In some possible embodiments, the aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the above-mentioned information recommendation method section of this specification, when said program product is executed on the terminal device.
Referring to fig. 6, a program product 800 for implementing the above-described method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be executed on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The program in the computer storage medium realizes the steps of the information recommendation method when being executed by the processor, and therefore, the computer storage medium can also obtain the technical effects of the information recommendation method.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (6)
1. An information recommendation method is characterized by comprising the following steps:
Acquiring commodity information of an alternative commodity, determining an associated commodity of the alternative commodity, and acquiring commodity information of the associated commodity;
selecting a recommended commodity from the candidate commodity according to commodity information of the candidate commodity and commodity information of the related commodity to obtain commodity information of the recommended commodity;
performing display rule verification on the recommended commodity;
if the recommended commodity passes the verification of the display rule, displaying commodity information of the recommended commodity;
if the recommended commodity does not pass the verification of the display rule, acquiring commodity information of the related commodity of the recommended commodity and displaying the commodity information;
Wherein, according to the commodity information of the commodity candidate and the commodity information of the related commodity, selecting the commodity candidate from the commodity candidate, comprising the following steps:
Determining characteristic data of the alternative commodity according to commodity information of the alternative commodity and commodity information of the related commodity;
Calculating the recommendation degree score of the candidate commodity by adopting a preset recommendation degree scoring rule according to the characteristic data of the candidate commodity;
Selecting a recommended commodity from the candidate commodities according to the recommendation degree score;
Wherein, according to commodity information of the commodity candidate and commodity information of the related commodity, determining characteristic data of the commodity candidate comprises the following steps:
Extracting characteristic values of each characteristic category from commodity information of the candidate commodity;
Extracting the characteristic value of the characteristic category from the commodity information of the related commodity for the characteristic category which does not comprise the characteristic value in the candidate commodity;
Combining the characteristic value extracted from the commodity information of the candidate commodity with the characteristic value extracted from the commodity information of the related commodity to obtain the characteristic data of the candidate commodity;
after determining the characteristic data of the candidate commodity, the method further comprises the following steps:
determining the characteristic category corresponding to each associated commodity in the characteristic data of each candidate commodity to be used as the contribution characteristic category of each associated commodity;
summing the feature weights corresponding to the corresponding contribution feature categories of the associated commodities to obtain the contribution values of the associated commodities;
sorting the related commodities according to the contribution value from large to small;
after the recommended commodity is subjected to the display rule verification, if the recommended commodity does not pass the display rule verification, executing the following steps:
sequentially selecting an associated commodity from front to back according to the contribution value sequence of the associated commodity;
Judging whether the selected associated commodity can pass through the verification of the display rule, if so, displaying commodity information of the selected associated commodity, otherwise, continuing to select the next associated commodity, and then executing the current step in a circulating way.
2. The information recommendation method according to claim 1, wherein determining the associated commodity of the candidate commodity comprises the steps of:
calculating the similarity of the alternative commodity and other commodities;
And taking other commodities with the similarity larger than a preset similarity threshold value with the alternative commodity or other commodities with the highest similarity with the alternative commodity as related commodities of the alternative commodity.
3. The information recommendation method according to claim 1, wherein the recommended commodity is subjected to a presentation rule verification, comprising the steps of:
inquiring the stock of the recommended commodity, and judging whether the quantity of the stock of the recommended commodity meets the requirement of display rule verification.
4. An information recommendation system for implementing the information recommendation method of any one of claims 1 to 3, the system comprising:
The information acquisition module is used for acquiring commodity information of the alternative commodity, determining an associated commodity of the alternative commodity and acquiring commodity information of the associated commodity;
the recommendation selection module is used for selecting recommended commodities from the candidate commodities according to commodity information of the candidate commodities and commodity information of the associated commodities to obtain commodity information of the recommended commodities;
The rule verification module is used for carrying out display rule verification on the recommended commodity;
The information display module displays commodity information of the recommended commodity if the recommended commodity passes the verification of the display rule; and if the recommended commodity does not pass the verification of the display rule, the information display module acquires commodity information of the related commodity of the recommended commodity and displays the commodity information.
5. An information recommendation device, characterized by comprising:
A processor;
a memory having stored therein executable instructions of the processor;
Wherein the processor is configured to perform the steps of the information recommendation method of any of claims 1 to 3 via execution of the executable instructions.
6. A computer-readable storage medium storing a program, characterized in that the program when executed by a processor implements the steps of the information recommendation method of any one of claims 1 to 3.
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