CN113763134A - Information recommendation method, system, device and storage medium - Google Patents
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Abstract
The invention provides an information recommendation method, a system, equipment and a storage medium, wherein the method comprises the following steps: acquiring commodity information of an alternative commodity, determining a related commodity of the alternative commodity, and acquiring the commodity information of the related commodity; selecting a recommended commodity from the alternative commodities according to the commodity information of the alternative commodities and the commodity information of the associated commodities to obtain the commodity information of the recommended commodity; carrying out 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 is not verified by the display rule, acquiring commodity information of the associated commodity of the recommended commodity and displaying the commodity information. The invention keeps the logic of the whole process reasonable and consistent 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 relatively independent links are recommended and displayed, and the main interactive data are type and ID. The recommended factors are independent from or independent of the elements displayed. For the commodities with thin characteristics, the recommendation layer is usually associated with other commodities with rich characteristics as compensation commodities to obtain more calculation factors; for high real-time and low-inventory goods, the display layer usually sets a bottom-keeping logic to cope with inventory changes. Therefore, when a commodity with both the characteristics of rareness and high real-time performance is used, the compensation of the current recommendation layer and the bottom guarantee of the display layer are independent, so that the logic on the whole process is inconsistent, and the display result of the client is unreasonable.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide an information recommendation method, system, device and storage medium, which keep the logic of the whole process reasonable and consistent by introducing a common intermediate commodity during information recommendation and information display.
The embodiment of the invention provides an information recommendation method, which comprises the following steps:
acquiring commodity information of an alternative commodity, determining a related commodity of the alternative commodity, and acquiring the commodity information of the related commodity;
selecting a recommended commodity from the alternative commodities according to the commodity information of the alternative commodities and the commodity information of the associated commodities to obtain the commodity information of the recommended commodity;
carrying out 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 is not verified by the display rule, acquiring commodity information of the associated commodity of the recommended commodity and displaying the commodity information.
In some embodiments, selecting a recommended commodity from the candidate commodities according to the commodity information of the candidate commodity and the commodity information of the associated commodity includes:
determining feature data of the alternative commodity according to the commodity information of the alternative commodity and the commodity information of the associated commodity;
calculating recommendation degree scores of the alternative commodities by adopting a preset recommendation degree score rule according to the feature data of the alternative commodities;
and selecting recommended commodities from the candidate commodities according to the recommendation degree score.
In some embodiments, selecting a recommended commodity from the candidate commodities according to the commodity information of the candidate commodity and the commodity information of the associated commodity includes:
determining feature data of the alternative commodity according to the commodity information of the alternative commodity and the commodity information of the associated commodity;
inputting the feature vectors of the candidate commodities 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 feature data of the candidate commodity, the method further includes the following steps:
calculating the contribution value of each associated commodity to the feature data of the candidate commodity;
and sorting the associated commodities from large to small according to the contribution value.
In some embodiments, after the verification of the display rule for the recommended merchandise, if the recommended merchandise is not verified by the display rule, the following steps are performed:
sequentially selecting a related commodity from front to back according to the contribution value sequence of the related commodity;
and judging whether the selected associated commodity can pass the verification of the display rule, if so, displaying the commodity information of the selected associated commodity, otherwise, continuously selecting the next associated commodity, and then circularly executing the current step.
In some embodiments, calculating the contribution value of each associated commodity to the feature data of the candidate commodity comprises the following steps:
determining a feature category corresponding to each associated commodity in the feature data of each candidate commodity as a contribution feature category of each associated commodity;
for each related commodity, the number of the contributing feature categories is used as the contribution value of the related commodity.
In some embodiments, calculating the contribution value of each associated commodity to the feature data of the candidate commodity comprises the following steps:
determining a feature category corresponding to each associated commodity in the feature data of each candidate commodity as a contribution feature category of each associated commodity;
and for each associated commodity, summing the feature weights corresponding to each contribution feature type corresponding to the associated commodity to obtain the contribution value of the associated commodity.
In some embodiments, determining the associated commodity of the candidate commodity comprises:
calculating the similarity between the alternative commodity and other commodities;
and taking other commodities with the similarity greater than a preset similarity threshold value with the candidate commodity or other commodities with the highest similarity and preset quantity with the highest similarity with the candidate commodity as the associated commodities of the candidate commodity.
In some embodiments, the display rule verification of the recommended merchandise includes the following steps:
and 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, which is used for realizing the information recommendation method and comprises the following steps:
the information acquisition module is used for acquiring commodity information of an alternative commodity, determining a related commodity of the alternative commodity and acquiring the commodity information of the related commodity;
the recommendation selection module is used for selecting recommended commodities from the alternative commodities according to the commodity information of the alternative commodities and the commodity information of the associated commodities to obtain the commodity information of the recommended commodities;
the rule verification module is used for verifying the display rule of the recommended commodity;
the information display module displays the commodity information of the recommended commodity if the recommended commodity passes the verification of the display rule; and if the recommended commodity is not verified by the display rule, the information display module acquires commodity information of the associated commodity of the recommended commodity and displays the commodity information.
An embodiment of the present invention further provides an information recommendation device, including:
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, and the program realizes the steps of the information recommendation method when being executed by a processor.
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, the information recommendation system, the information recommendation equipment and the storage medium have the following beneficial effects:
in the information recommending and selecting process, the related commodities serving as the intermediate commodities are introduced, in the information displaying process, when the recommended commodities cannot pass the display rule verification, the related commodities are selected as the guaranteed-bottom commodities to be displayed, and by introducing the common intermediate commodities in the information recommending and information displaying processes, the logic reasonableness and the consistency of the whole process are kept.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
FIG. 1 is a flow chart of an information recommendation method according to an embodiment of the invention;
FIG. 2 is a flow chart of selecting a recommended good from the alternative goods according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating verification of display rules for recommended merchandise, according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an information recommendation system according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an information recommendation apparatus according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram 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. Example embodiments may, however, be embodied in many different 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 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 their repetitive description 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 the form of 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 charts shown in the drawings are merely illustrative and do not necessarily include all of the steps. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
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 a related commodity of the alternative commodity, and acquiring the commodity information of the related commodity;
s200: selecting a recommended commodity from the alternative commodities according to the commodity information of the alternative commodities and the commodity information of the associated commodities to obtain the commodity information of the recommended commodity;
s300: carrying out 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 is not verified by the display rule, acquiring commodity information of the associated commodity of the recommended commodity and displaying the commodity information.
In the information recommending and selecting process, the associated commodities serving as intermediate commodities are introduced through the steps S100 and S200, in the information displaying process, firstly, the displaying rule verification of the recommended commodities is carried out through the step S300, when the recommended commodities pass the displaying rule verification, the commodity information of the recommended commodities is directly displayed through the step S400, when the recommended commodities cannot pass the displaying rule verification, the associated commodities are selected as bottom-guaranteeing commodities to be displayed through the step S500, and by introducing the common intermediate commodities in the information recommending and information displaying processes, the logic of the whole process is kept reasonable and consistent.
In this embodiment, the step S100: determining the related commodities of the candidate commodities, comprising the following steps:
calculating the similarity between the candidate commodity and other commodities, wherein the similarity between the two commodities is calculated at this time, and for example, the cosine similarity, the euclidean distance and the like of the feature vectors of the two commodities are calculated, or the similarity is calculated based on the number of the same feature categories of the two commodities; for example, a total of 80 feature categories are preset, feature values of the candidate commodities corresponding to the feature categories are obtained, another commodity is selected, the feature values corresponding to the feature categories are obtained, if no data exists when a commodity corresponds to a feature category, the feature values are set as default values, then the feature values of the feature categories of the candidate commodities are combined to obtain feature vectors, the feature vectors are obtained by combining the feature values of the feature categories of the other commodities, and the cosine similarity, the Euclidean distance and the like of the two feature vectors are calculated to serve as the similarity of the two commodities; or, counting the number of feature categories having the same feature value between the candidate product and one other product, for example, if the feature values of fifteen feature categories of the candidate product are the same as the feature values of one other product, the similarity between the other product and the candidate product is 15, or 15/80;
and taking other commodities with the similarity greater than a preset similarity threshold value with the candidate commodity or other commodities with the highest similarity and preset quantity with the highest similarity with the candidate commodity as the associated commodities of the candidate commodity.
For example, the invention can be used for recommending and displaying hotel group purchase/second killing goods. Due to the fact that the hotel group purchase/second killing commodity is small in inventory and short in online time, each independent commodity is difficult to accumulate enough elements which can be used as recommended calculation factors, and the hotel group purchase/second killing commodity is a typical commodity with thin characteristics and high real-time performance. When the group buying/second commodity killing feature is insufficient, the recommendation process uses the hotel commodity related to the commodity, or the group buying/second commodity killing of the current date/current date and hotel, or the group buying/second commodity killing of the current date/current date and star level same house type as the calculation factor for obtaining compensation of the related commodity.
As shown in fig. 2, in this embodiment, the step S200: selecting a recommended commodity from the candidate commodities according to the commodity information of the candidate commodity and the commodity information of the associated commodity, comprising the following steps:
s210: determining feature data of the alternative commodity according to the commodity information of the alternative commodity and the commodity information of the associated commodity;
specifically, step S210 includes the steps of:
s211: extracting characteristic values of all characteristic categories from the commodity information of the candidate commodities;
s212: extracting the feature value of the feature category from the commodity information of the associated commodity for the feature category which does not include the feature value in the candidate commodity;
s213: combining the characteristic values extracted from the commodity information of the candidate commodity and the characteristic values extracted from the commodity information of the associated commodity to obtain characteristic data of the candidate commodity;
this feature class, i.e., the calculation factor when corresponding to the score to be calculated subsequently, may include, for example, geographic location, price, star rating, area, whether breakfast is present, etc.;
s220: calculating recommendation degree scores of the alternative commodities by adopting a preset recommendation degree score rule according to the feature data of the alternative commodities;
the preset recommendation degree scoring rule may be, for example, a preset calculation formula, wherein feature values of feature categories are used as variables, and for a candidate commodity, the feature values in feature data of the candidate commodity are filled into the calculation formula, so that a corresponding recommendation degree score is obtained;
for example, the calculation formula may be a weighted summation 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, thereby obtaining the recommendation score. When calculating the recommendation degree score, the calculation can be further performed by combining the historical behavior data of different users. For example, calculating a feature value of each feature category corresponding to the user according to historical behavior data (purchase data, browsing data, click data, and the like) of the user;
s230: and selecting recommended commodities from the candidate commodities according to the recommendation degree score.
For example, the candidate commodities may be sorted in order from high to low according to the recommendation degree score, and the candidate commodities with a preset commodity recommendation number or recommendation degree score larger than a preset score threshold value may be selected from front to back as the recommended commodities.
In another alternative embodiment, the step S200: selecting a recommended commodity from the candidate commodities according to the commodity information of the candidate commodity and the commodity information of the associated commodity, comprising the following steps:
determining feature data of the alternative commodity according to the commodity information of the alternative commodity and the commodity information of the associated commodity;
inputting the feature vectors of the candidate commodities 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 product selection model may be a machine learning model such as a convolutional neural network. 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 probability output by the model as a recommendation score. When the recommendation degree score is calculated through the model, the user feature data may also be combined, for example, the user feature data and the feature data of the candidate commodity are input into a dual-input machine learning model, the machine learning model outputs the matching probability of the user feature data and the feature data of the candidate commodity, and the matching probability is used as the recommendation degree 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 feature data of the candidate commodity;
s250: and sorting the associated commodities from large to small according to the contribution value.
As shown in fig. 3, in this embodiment, the step S300: after the display rule verification is carried out on the recommended commodity, if the recommended commodity does not pass the display rule verification, the following steps are carried out:
s310: sequentially selecting a related commodity from front to back according to the contribution value sequence of the related commodity;
s320: judging whether the selected associated commodity can pass the verification of the display rule;
if so, continue to step S400: displaying commodity information of the selected associated commodity;
otherwise, continue to step S330: and continuing to select the next associated commodity according to the contribution value sorting of the associated commodities, and then continuing to the step S320.
In this embodiment, the step S240: calculating the contribution value of each associated commodity to the feature data of the candidate commodity, wherein the method comprises the following steps:
determining a feature category corresponding to each associated commodity in the feature data of each candidate commodity as a contribution feature category of each associated commodity;
for example, there are a total of 80 feature categories, the candidate product has feature values of only thirty feature categories, and if feature values of ten feature categories are provided in one associated product, there are ten contributing feature categories of the associated product;
for each related commodity, the number of the contribution feature categories of the related commodity is used as the contribution value of the related commodity, and for example, if the contribution feature category of one related commodity is ten, the contribution value of the related commodity is 10.
In another embodiment, the step S240: calculating the contribution value of each associated commodity to the feature data of the candidate commodity, wherein the method comprises the following steps:
determining a feature category corresponding to each associated commodity in the feature data of each candidate commodity as a contribution feature category of each associated commodity;
and for each associated commodity, summing the feature weights corresponding to each contribution feature type corresponding to the associated commodity to obtain the contribution value of the associated commodity. That is, in calculating the contribution value, not only the number of the contribution feature categories of each associated product but also the weight values of different feature categories are considered, and the weight value of a more important feature category is higher.
In this embodiment, the step S300: and verifying the display rule of the recommended commodity, including performing real-time verification on the recommended commodity, wherein the real-time verification indicates that the inventory quantity of the recommended commodity can meet the requirement of the display rule verification. Specifically, the step S300 includes the steps of:
and 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 insurance bottom of the display layer is determined based on the data of the current recommendation compensation. And aiming at a commodity with a certain rarefied characteristic, when the recommendation layer obtains the compensation calculation factor by correlating related commodities, incorporating one/a batch of related commodities which have the largest comprehensive influence on the final result into recommendation data for issuing. The display layer is used for verifying the normal effectiveness of the commodities, and due to the fact that the inventory of the commodities is low and the requirement on real-time performance is high, the high probability of the display layer can enter display bottom-keeping logic. And when entering the bottom protection logic, the display layer reads the associated commodity data in the recommended issuing data and uses the associated commodity recommended to be compensated at the current time as a bottom protection display commodity. Thereby achieving the consistency of the recommendations with the presented logic.
The above hotel group purchase/sec killing scenario is taken as an example for explanation. After the recommended group purchase/second killing commodity is selected in step S200, not only the commodity information of the group purchase/second killing commodity is issued, but also the commodity information of the associated commodity is issued along with the group purchase/second killing commodity, and in the issued associated commodity list, the associated commodities are sorted according to the magnitude of the contribution value, that is, sorted according to the degree of influence of the associated commodity on the recommendation degree score.
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 bottom-preserving logic of display. In the bottom protection logic of the commodity, the display layer sequentially performs validity check according to the associated commodity list issued by the recommendation layer, and uses the first associated commodity passing the validity check as bottom protection display, so as to realize that the display logic and the recommendation logic keep consistent interpretability.
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 configured to acquire commodity information of an alternative commodity, determine a related commodity of the alternative commodity, and acquire commodity information of the related commodity;
a recommendation selection module M200, configured to select a recommended commodity from the candidate commodities according to the commodity information of the candidate commodity and the commodity information of the associated commodity, so as to obtain commodity information of the recommended commodity;
the rule verification module M300 is used for verifying the display rule of the recommended commodity;
an information display module M400, which displays the commodity information of the recommended commodity if the recommended commodity passes the verification of the display rule; and if the recommended commodity is not verified by the display rule, the information display module acquires commodity information of the associated commodity of the recommended commodity and displays the commodity information.
In the information recommendation and selection process, the information acquisition module M100 and the recommendation and selection module M200 introduce associated commodities serving as intermediate commodities, in the information display process, firstly, the rule verification module M300 is used for verifying the display rule of the recommended commodities, when the recommended commodities pass the verification of the display rule, the information display module M400 is used for directly displaying the commodity information of the recommended commodities, when the recommended commodities cannot pass the verification of the display rule, the information display module M400 is used for selecting the associated commodities to serve as bottom-protected commodities for display, and common intermediate commodities are introduced during information recommendation and information display, so that the logic of the whole process is kept reasonable and consistent.
In the information recommendation system of the present invention, the functions of each module may be implemented by using the specific implementation manners of each step in the information recommendation method, for example, the information obtaining module M100 may obtain the related information by using the specific implementation manner of step S100, the recommendation selecting module M200 may select the recommended product by using the specific implementation manner of step S200, the rule verifying module M300 may verify the display rule by using the specific implementation manner of step S300, and the information display module M400 may display the product by using the specific implementation manner of step S400, which is not described again here.
The embodiment of the invention also provides information recommendation 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.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally 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 only an example and should not bring any limitation to the functions and the scope of use of the 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. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the information recommendation method section above in this specification. For example, the processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory 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 of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
In the information recommendation apparatus, the program in the memory is executed by the processor to implement the steps of the information recommendation method, and therefore, the apparatus can also 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, and the program realizes the steps of the information recommendation method when being executed by a processor. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the invention described in the information recommendation method section above of this specification when the program product is executed on the terminal device.
Referring to fig. 6, a program product 800 for implementing the above 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 in this regard and, in the present 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. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc 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 propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and 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 for aspects 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 and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, 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., through the internet using an internet service provider).
The program in the computer storage medium implements the steps of the information recommendation method when executed by a processor, and thus the computer storage medium can also obtain the technical effects of the information recommendation method.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (12)
1. An information recommendation method is characterized by comprising the following steps:
acquiring commodity information of an alternative commodity, determining a related commodity of the alternative commodity, and acquiring the commodity information of the related commodity;
selecting a recommended commodity from the alternative commodities according to the commodity information of the alternative commodities and the commodity information of the associated commodities to obtain the commodity information of the recommended commodity;
carrying out 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 is not verified by the display rule, acquiring commodity information of the associated commodity of the recommended commodity and displaying the commodity information.
2. The information recommendation method according to claim 1, wherein selecting a recommended commodity from the candidate commodities based on the commodity information of the candidate commodity and the commodity information of the related commodity, comprises:
determining feature data of the alternative commodity according to the commodity information of the alternative commodity and the commodity information of the associated commodity;
calculating recommendation degree scores of the alternative commodities by adopting a preset recommendation degree score rule according to the feature data of the alternative commodities;
and selecting recommended commodities from the candidate commodities according to the recommendation degree score.
3. The information recommendation method according to claim 1, wherein selecting a recommended commodity from the candidate commodities based on the commodity information of the candidate commodity and the commodity information of the related commodity, comprises:
determining feature data of the alternative commodity according to the commodity information of the alternative commodity and the commodity information of the associated commodity;
inputting the feature vectors of the candidate commodities 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.
4. The information recommendation method according to claim 2 or 3, further comprising, after determining the feature data of the candidate product, the steps of:
calculating the contribution value of each associated commodity to the feature data of the candidate commodity;
and sorting the associated commodities from large to small according to the contribution value.
5. The information recommendation method according to claim 4, wherein after the display rule verification is performed on the recommended product, if the recommended product fails the display rule verification, the following steps are performed:
sequentially selecting a related commodity from front to back according to the contribution value sequence of the related commodity;
and judging whether the selected associated commodity can pass the verification of the display rule, if so, displaying the commodity information of the selected associated commodity, otherwise, continuously selecting the next associated commodity, and then circularly executing the current step.
6. The information recommendation method according to claim 4, wherein calculating the contribution value of each of the related commodities to the feature data of the candidate commodity comprises:
determining a feature category corresponding to each associated commodity in the feature data of each candidate commodity as a contribution feature category of each associated commodity;
for each related commodity, the number of the contributing feature categories is used as the contribution value of the related commodity.
7. The information recommendation method according to claim 4, wherein calculating the contribution value of each of the related commodities to the feature data of the candidate commodity comprises:
determining a feature category corresponding to each associated commodity in the feature data of each candidate commodity as a contribution feature category of each associated commodity;
and for each associated commodity, summing the feature weights corresponding to each contribution feature type corresponding to the associated commodity to obtain the contribution value of the associated commodity.
8. The information recommendation method according to claim 1, wherein determining the related product of the candidate product comprises the steps of:
calculating the similarity between the alternative commodity and other commodities;
and taking other commodities with the similarity greater than a preset similarity threshold value with the candidate commodity or other commodities with the highest similarity and preset quantity with the highest similarity with the candidate commodity as the associated commodities of the candidate commodity.
9. The information recommendation method according to claim 1, wherein the verification of the display rule of the recommended commodity comprises the steps of:
and 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.
10. An information recommendation system for implementing the information recommendation method of any one of claims 1 to 9, the system comprising:
the information acquisition module is used for acquiring commodity information of an alternative commodity, determining a related commodity of the alternative commodity and acquiring the commodity information of the related commodity;
the recommendation selection module is used for selecting recommended commodities from the alternative commodities according to the commodity information of the alternative commodities and the commodity information of the associated commodities to obtain the commodity information of the recommended commodities;
the rule verification module is used for verifying the display rule of the recommended commodity;
the information display module displays the commodity information of the recommended commodity if the recommended commodity passes the verification of the display rule; and if the recommended commodity is not verified by the display rule, the information display module acquires commodity information of the associated commodity of the recommended commodity and displays the commodity information.
11. An information recommendation apparatus 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 9 via execution of the executable instructions.
12. A computer-readable storage medium storing a program, wherein the program is configured to implement the steps of the information recommendation method according to any one of claims 1 to 9 when executed by a processor.
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