CN109389442A - Method of Commodity Recommendation and device, storage medium and electric terminal - Google Patents
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Abstract
This disclosure relates to technical field of data processing, and in particular to a kind of Method of Commodity Recommendation, a kind of device for recommending the commodity, a kind of storage medium and a kind of electric terminal.The Method of Commodity Recommendation includes: the user's similarity for calculating user and each user to be recommended；User's similarity result is screened, in order to obtain associated user's set and corresponding associated user's commodity rating matrix and dependent merchandise set；Calculate the commodity degree of correlation in the dependent merchandise set between each commodity；According to user to be recommended described in dependent merchandise set described in associated user's commodity rating matrix and the commodity relatedness computation do not score commodity scoring estimation；Appraisal result according to each commodity is ranked up, and the ranking results for choosing preset quantity are sent to the user to be recommended.The disclosure can effectively promote the accuracy and matching degree of recommendation results, so that hobby and demand that Recommendations are more close to the users effectively promote the accuracy and matching degree of recommendation results.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a commodity recommendation method, a commodity recommendation apparatus, a storage medium, and an electronic terminal.
Background
In recent years, with the rapid development of electronic commerce, the number of users and the number of commodities are increased rapidly, so that users often cannot find needed information or commodities from mass resources quickly and accurately, that is, the problem of information overload exists. At present, three methods are mainly used for solving the information overload problem: navigation method, search method and recommendation method. Among them, the recommendation method is a method which is particularly widely used at present. The most widely used of the existing recommendation algorithms is the Collaborative Filtering (CF), the two most commonly used types of which are: user-based collaborative filtering algorithms (User CF) and Item-based collaborative filtering algorithms (Item CF).
However, both of the above algorithms have certain drawbacks: 1) the collaborative filtering algorithm based on the users often simply calculates the similarity of the users through a scoring matrix, and the problem of low accuracy exists. Because the scoring matrix often has the problem of data sparsity, the relevance calculation is not accurate when the relevance calculation is carried out by the scoring matrix. In addition, the attribute of the user is not considered in the correlation calculation, so that the calculation result is often greatly different from the real situation. 2) The project-based collaborative filtering algorithm does not always consider hidden relations among commodities, and commodity relevance is reduced. Since the domain knowledge of the commodities is not used in the algorithm, all commodity items are considered to be independent and irrelevant, and the real relevancy among the commodity items can be influenced only by calculating through the scoring matrix, so that the final recommendation result is inaccurate.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a commodity recommendation method, a commodity recommendation apparatus, a storage medium, and an electronic terminal, thereby overcoming, at least to some extent, one or more of the problems due to the limitations and disadvantages of the related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a commodity recommendation method including:
calculating the user similarity between the user to be recommended and each user;
screening the user similarity result so as to obtain a relevant user set, a corresponding relevant user commodity scoring matrix and a relevant commodity set;
calculating commodity relevance among commodities in the relevant commodity set;
calculating the grade estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity grade matrix of the relevant user and the commodity relevancy to obtain the grade estimation matrix of the relevant commodities;
and sorting according to the grading result of each commodity, and selecting a preset number of sorting results to send to the user to be recommended.
In an exemplary embodiment of the present disclosure, the calculating the user similarity between the user to be recommended and each user according to the user tag information includes:
acquiring a user set in a user-commodity scoring matrix, and describing each user through N different label information:
ui＝[xi1... xil... xiN]T
wherein x isilFor user uiL is more than or equal to 1 and less than or equal to N;
the correlation between any two users is:
wherein R isuser(ui,uj) For user uiWith user ujN is a positive integer.
In an exemplary embodiment of the present disclosure, the user tag information includes: any combination of basic information, interesting commodities, access history, purchasing behavior and collection records.
In an exemplary embodiment of the present disclosure, the calculating the commodity relevance between the commodities includes:
acquiring commodity classification structure information of each commodity;
calculating the commodity relevancy between any two commodities according to the classification structure information, wherein the specific formula is as follows:
wherein R isgoods(gi,gj)∈[0，1]；d(gi) Is a commodity giPath Length to root node, LCA (g)i,gj) Is a commodity giAnd goods gjThe common ancestor with the largest path.
In an exemplary embodiment of the present disclosure, the calculating, according to the relevant user commodity rating matrix and the commodity relevancy degree, a rating estimation of the to-be-recommended user non-rated commodity in the relevant commodity set includes:
screening the commodities with the product relevance larger than a preset value for the unscored commodities so as to obtain an unscored related commodity set of the unscored commodities and a corresponding unscored commodity relevance set;
calculating the comprehensive score of the user to be recommended on the unscored commodities based on the relevant user commodity scoring matrix and the unscored commodity relevancy set, wherein the specific formula is as follows:
wherein, s (u)i,gj) For the user to be recommended to mark the unscored commodity gjThe score estimation of (2); rgoods(gk,gj) Is a commodity gkWith commercial product gjThe degree of correlation of (c); gsThe related commodities are collected.
In an exemplary embodiment of the present disclosure, after calculating a score estimation matrix for obtaining relevant commodities after completing the score estimation of the unscored commodities, the commodity recommendation method further includes:
calculating the comprehensive scores of the user to be recommended on the commodities in the relevant commodity set according to the user similarity and the score estimation matrix of the relevant commodities, wherein the specific formula is as follows:
wherein,for the user u to be recommendediFor commodity gjThe composite score of (4); s (u)k,gj) To useHuu (household)kTo-be-pushed commodity gjScoring of (4);for the user u to be recommendediA mean value of the scores for each of said commodities.
According to a second aspect of the present disclosure, there is provided an article recommendation device including:
the user similarity processing module is used for calculating the user similarity between the user to be recommended and each user according to the user tag information;
the user screening module is used for screening the user similarity result to obtain a related user set, a corresponding related user commodity scoring matrix and a related commodity set;
the commodity relevancy processing module is used for calculating the commodity relevancy among commodities in the relevant commodity set;
the score estimation module is used for calculating the score estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity score matrix of the relevant user and the commodity correlation degree so as to obtain the score estimation matrix of the relevant commodities;
and the recommendation processing module is used for sorting according to the grading result of each commodity and selecting a preset number of sorting results to recommend to the user to be recommended.
In an exemplary embodiment of the present disclosure, the article recommendation apparatus further includes:
the comprehensive grading module is used for screening the commodities with the product relevance larger than a preset value for the unscored commodities to obtain an unscored related commodity set of the unscored commodities and a corresponding unscored commodity relevance set;
and calculating the comprehensive score of the user to be recommended on the unscored commodities based on the relevant user commodity scoring matrix and the unscored commodity relevance set.
According to a third aspect of the present disclosure, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described article recommendation method.
According to a fourth aspect of the present disclosure, there is provided an electronic terminal comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the following via execution of the executable instructions:
calculating the user similarity between the user to be recommended and each user by using the user tag information;
screening the user similarity result so as to obtain a relevant user set, a corresponding relevant user commodity scoring matrix and a relevant commodity set;
calculating commodity relevance among commodities in the relevant commodity set;
calculating the grade estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity grade matrix of the relevant user and the commodity relevancy to obtain the grade estimation matrix of the relevant commodities;
and sorting according to the grading result of each commodity, and selecting a preset number of sorting results to send to the user to be recommended.
In the commodity recommendation method provided by the embodiment of the disclosure, the user similarity between the user to be recommended and each user is calculated, the users are screened according to the user similarity, then the relevance between the commodities in the corresponding relevant commodity set after screening is calculated, and finally the commodities are scored by combining the user similarity and the commodity relevance, and the scoring results are sorted, and the sorting results with the preset number are recommended to the users. By simultaneously scoring the products by utilizing the user similarity and the product correlation, the attributes of the users and the correlation among the products are fully considered, and the accuracy and the matching degree of the recommendation result are ensured, so that the recommended commodities are closer to the preference and the demand of the users, and the accuracy and the matching degree of the recommendation result are effectively improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 schematically illustrates a merchandise recommendation method in an exemplary embodiment of the disclosure;
FIG. 2 is a schematic diagram illustrating a user similarity calculation method in an exemplary embodiment of the present disclosure;
FIG. 3 schematically illustrates a product similarity calculation method in an exemplary embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating an article classification structure according to an exemplary embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating another merchandise recommendation method in an exemplary embodiment of the present disclosure;
fig. 6 schematically illustrates a composition diagram of an article recommendation device in an exemplary embodiment of the present disclosure;
FIG. 7 schematically illustrates another schematic view of an article recommendation device in an exemplary embodiment of the present disclosure;
fig. 8 schematically illustrates still another schematic diagram of an article recommendation device in an exemplary embodiment of the present disclosure.
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 exemplary embodiment first provides a commodity recommendation method, which can be applied to an e-commerce website. The recommendation method can establish a user model by analyzing historical behavior data of the user under the condition that the user demand is unclear, and then recommend corresponding information according to the interest and the demand of the user. By the method, a user can conveniently and quickly acquire required information, and the effective utilization rate of the information is improved while the time for information screening is saved. Referring to fig. 1, the above-described goods recommendation method may include the steps of:
s1, calculating the user similarity of the user to be recommended and each user;
s2, screening the user similarity results to obtain a relevant user set, a corresponding relevant user commodity scoring matrix and a relevant commodity set;
s3, calculating commodity relevance among commodities in the relevant commodity set;
s4, calculating the grade estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity grade matrix of the relevant user and the commodity relevancy to obtain the grade estimation matrix of the relevant commodities;
and S5, sorting according to the grading result of each commodity, selecting a preset number of sorting results and sending the sorting results to the user to be recommended.
In the commodity recommendation method provided by the exemplary embodiment, the products are scored by simultaneously utilizing the user similarity and the product correlation, the attributes of the users and the correlation among the products are fully considered, the accuracy and the matching degree of the recommendation result are ensured, and the recommended commodities are closer to the preference and the requirements of the users.
Hereinafter, the steps of the method in the present exemplary embodiment will be described in more detail with reference to the drawings and examples.
Step S1, calculating a user u to be recommendediUser similarity to each user.
In this exemplary embodiment, referring to fig. 2, specifically, the following steps may be included in calculating the user similarity:
step S11, a user set U in a user-commodity scoring matrix is obtained.
Step S12, describing each user by N different label information,
ui＝[xi1... xil... xiN]T(1)
wherein x isilFor user uiL is more than or equal to 1 and less than or equal to N;
step S13, calculating the correlation between any two users, the specific formula is:
wherein R isuser(ui,uj) For user uiWith user ujN is a positive integer.
User u to be recommendediThe user similarity to the user set U can be expressed as:
Ruser(ui,U)＝[Ruser(ui,u1) ... Ruser(ui,uj) ... Ruser(ui,uM)](3)
in the above formula (2), the correlation between users, R, can be calculated using the cosine formulauser(ui,uj) The larger the calculation result of (b), the more the user u is indicatediWith user ujThe higher the similarity between them.
By fully describing the users by using the label information, when the relevance between every two users is calculated, the similarity between the users can be compared, judged and scored from multiple dimensions, and the accuracy and reliability of the calculation result of the similarity of the users are improved. Meanwhile, the method can effectively avoid inaccurate calculation of the user similarity caused by sparse data in the scoring matrix.
In other examples of the present disclosure, the user tag information described above may include, but is not limited to: the label data such as basic information, interested commodities, access history, purchasing behavior and collection records, etc., and the user label information may be a combination of any of the above. Wherein, the basic information may include: sex, age, work, hobby and place of attribution. The above-mentioned interest item may be a kind of item, such as sports equipment, kitchen ware, etc., or may be an item, such as: basketball, dish washers, etc.
And step S2, screening the user similarity result so as to obtain a related user set, and a corresponding related user commodity scoring matrix and a related commodity set.
After the correlation degree between the user to be recommended and each user is calculated, the preset value tau of the user similarity can be useduserScreening each user when R isuser(ui,uj)≥τuserWhen, i.e. when user ujWith the user u to be recommendediIf the user similarity between the users is greater than the preset value, the user u is determinedjFall into a set of related users Ui. At the same time, according to the related user set UiCan obtain the corresponding related user set UiRelevant user-commodity scoring matrix SiAnd corresponding related goods set Gs。
Step S3, calculating the related commodity set GsCommodity correlation degree R between commodities in itg。
In this example embodiment, the calculating the commodity relevance may specifically include:
step S31, product classification structure information of each of the products is acquired.
Step S32, calculating the commodity correlation between any two commodities according to the classification structure information, wherein the specific formula is as follows:
wherein R isgoods(gi,gj)∈[0，1](ii) a When g isi＝gjWhen R isgoods(gi,gj)＝1。d(gi) Is a commodity giPath Length to root node, LCA (g)i,gj) Is a commodity giAnd goods gjThe common ancestor with the largest path.
For e-commerce websites, the classification relationship of the commodities is relatively fixed for a period of time, and at this time, each commodity can be represented by an inverted tree structure. Referring to fig. 4, the graph sequentially corresponds to classification information such as the first-level classification, the second-level classification, and the third-level classification of the commodity, and the lowest layer (leaf layer) identifies the commodity information. The relevance among the commodities is calculated by utilizing the classification relation of the commodities, the belonging field of each commodity and the relation among the commodities are fully considered, and the accuracy of the commodity relevance is effectively improved.
Step S4, according to the related user commodity scoring matrix SiAnd the commodity correlation degree RgCalculating the related goods set GsThe grade estimation of the unscored goods of the user to be recommended is carried out to obtain a grade estimation matrix S of related goods2。
Based on the above, in the present exemplary embodiment, the step S4 may specifically include:
step S41, screening related commodity set GsNeutralization unscored commodity gjThe commodity correlation degree of the commodity is more than the preset value so as to obtain the commodity g which is not scored convenientlyjUnscored related commodity set G with high commodity relevancesjAnd acquiring a corresponding unscored commodity relevance set Rgoods(gj,Gsj) Wherein the unscored related merchandise set GsjAnd a set of non-scored commodity relevance values RgoodsCan be respectively expressed as:
Gsj＝[g1... gi... gk](5)
Rgoods(gj,Gsj)＝[Rgoods(gj,g1) ... Rgoods(gj,gi) ... Rgoods(gj,gk)](6)
based on the related user commodity scoring matrix SiAnd the above unscored commodity relevance set Rgoods(gj,Gsj) Calculating the user u to be recommendediAnd estimating the score of the unscored commodity by a specific formula:
wherein, s (u)i,gj) For the user to be recommended to mark the unscored commodity gjThe score estimation of (2); s (u)i,gj) For the user to be recommended to commodity gkThe score estimation of (2); rgoods(gk,gj) Is a commodity gkWith commercial product gjThe degree of correlation of (c); gsThe related commodities are collected.
In the calculation of a user u to be recommendediFor unscored goods gjWhen the evaluation (S) is to be made, the related product group G is calculated in the above-mentioned step S3sEach commodity in the list and the unscored commodities gjThe commodity relevance is screened through preset commodity relevance preset values which are set in advance, and commodities with the commodity relevance larger than the preset values are added into the unscored related commodity set GsjAnd simultaneously acquiring corresponding unscored commodity relevance set Rgoods(gj,Gsj). After the rating estimation of the unscored commodities is obtained through the formula (7), a related commodity set G of the user to be recommended is obtainedsComplete score estimation matrix S2。
And step S5, sorting according to the grading result of each commodity, selecting a preset number of sorting results and sending the sorting results to the user to be recommended.
Obtaining the score estimation of each non-score commodity and a complete score estimation matrix S2Then, the commodities can be sorted from high to low according to the scores of the commodities; then, a certain number of commodities with higher rank can be selected according to a preset rule and recommended to the user to be recommended.
Based on the above, in other examples of the present disclosure, in order to further optimize the recommendation result, as shown in fig. 5, after the step S4 is completed, the method for recommending a good may further include:
step S5, according to the user similarity Ruser(uiU) and a score estimation matrix S of the related goods2Calculating the comprehensive score of the user to be recommended on each commodity in the relevant commodity set, wherein the specific formula is as follows:
wherein,for the user u to be recommendediFor commodity gjThe composite score of (4); s (u)k,gj) For user ukFor commodity gjScoring of (4);for the user ukA mean value of each of the merchandise scores in the user-merchandise score matrix;for the user u to be recommendediThe average value of each said item score in the user-item score matrix.
In other exemplary embodiments disclosed, in order to reduce the burden on the server and to improve the accuracy of the settlement result, in the above formula (8)May also be said user ukFor the related goods set GsThe average of the scores of each of said items,can be the user u to be recommendediFor the related goods set GsThe average of each of said product scores.
And step S6, sorting according to the grading result of each commodity, selecting a preset number of sorting results and sending the sorting results to the user to be recommended.
In this example, the user u to be recommended is weighted and calculated by introducing the mean value of all the product scores of the user and the corresponding user similarityiAnd the reliability and the accuracy of the commodity grading are further improved by comprehensively grading the commodities.
According to the commodity recommendation method, the user similarity is calculated by introducing the label information of the user in the calculation process, the inaccuracy of calculation of the user relevance caused by the sparse data in the scoring matrix is effectively avoided, the accuracy and the reliability of the calculation result are ensured, and the accuracy of user relation analysis is effectively improved. Meanwhile, the relevance among the commodities is calculated by utilizing the classification relation of the commodities, the relation between the commodities is fully considered, compared with the original calculation mode that each commodity is taken as an independent item, the calculation result can more accurately represent the relation among the commodities, and the effectiveness of the calculation result is improved.
When the product is recommended, the product is graded by simultaneously utilizing the user similarity and the product correlation, the attributes of the users and the correlation among the products are fully considered, the accuracy of the final recommendation result is ensured, the recommended goods are closer to the preference and the demand of the users, and then the efficient personalized service is provided for the users.
It is to be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to an exemplary embodiment of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Further, as shown in fig. 6, in the present exemplary embodiment, there is provided a product recommendation device 6 including: a user similarity processing module 61, a user screening module 62, a commodity correlation processing module 63, a score estimation module 64 and a recommendation processing module 65. Wherein:
the user similarity processing module 61 may be configured to calculate user similarities between the user to be recommended and each user according to the user tag information.
The user filtering module 62 may be configured to filter the user similarity result to obtain a relevant user set, and a corresponding relevant user commodity scoring matrix and a relevant commodity set.
The commodity relevance processing module 63 may be configured to calculate commodity relevance between commodities in the relevant commodity set;
the score estimation module 64 may be configured to calculate a score estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity score matrix of the relevant user and the commodity relevancy to obtain a score estimation matrix of the relevant commodities;
the recommendation processing module 65 may be configured to sort according to the scoring result of each commodity, and select a preset number of sorting results to recommend to the user to be recommended.
On the basis of the above, in order to further optimize the recommendation result, in the present exemplary embodiment, the article recommendation device 6 may further include:
the comprehensive grading module is used for screening the commodities with the product relevance larger than a preset value for the unscored commodities to obtain an unscored related commodity set of the unscored commodities and a corresponding unscored commodity relevance set; and calculating the comprehensive score of the user to be recommended on the unscored commodities based on the relevant user commodity scoring matrix and the unscored commodity relevance set.
The specific details of each module in the aforementioned commodity recommendation device have been described in detail in the corresponding commodity recommendation method, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
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" system.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 7. The electronic device 600 shown in fig. 7 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. 7, 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: the at least one processing unit 610, the at least one memory unit 620, a bus 630 connecting different system components (including the memory unit 620 and the processing unit 610), and a display unit 640.
Wherein the storage unit stores program code that is executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary methods" of the present specification. For example, the processing unit 610 may execute S1 shown in fig. 1: calculating the user similarity between the user to be recommended and each user; s2: screening the user similarity result so as to obtain a relevant user set, a corresponding relevant user commodity scoring matrix and a relevant commodity set; s3: calculating commodity relevance among commodities in the relevant commodity set; s4: calculating the grade estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity grade matrix of the relevant user and the commodity relevancy to obtain the grade estimation matrix of the relevant commodities; s5: and sorting according to the grading result of each commodity, and selecting a preset number of sorting results to send to the user to be recommended.
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.
Bus 630 may be 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 a local bus 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.), 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. As shown, the network adapter 660 communicates with the other modules of the electronic device 600 over 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.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 8, 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 run 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.
A computer readable signal 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 signal 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 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).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (10)
1. A method for recommending an article, comprising:
calculating the user similarity between the user to be recommended and each user;
screening the user similarity result so as to obtain a relevant user set, a corresponding relevant user commodity scoring matrix and a relevant commodity set;
calculating commodity relevance among commodities in the relevant commodity set;
calculating the grade estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity grade matrix of the relevant user and the commodity relevancy, and acquiring the grade estimation matrix of the relevant commodities;
and sorting according to the grading result of each commodity, and selecting a preset number of sorting results to send to the user to be recommended.
2. The commodity recommendation method according to claim 1, wherein the calculating of the user similarity between the user to be recommended and each user comprises:
acquiring a user set in a user-commodity scoring matrix, and describing each user through N different label information:
ui＝[xi1... xil... xiN]T
wherein x isilFor user uiL is more than or equal to 1 and less than or equal to N;
the correlation between any two users is:
wherein R isuser(ui,uj) For user uiWith user ujN is a positive integer.
3. The item recommendation method according to claim 1 or 2, wherein the user tag information includes: any combination of basic information, interesting commodities, access history, purchasing behavior and collection records.
4. The product recommendation method according to claim 1, wherein said calculating the product relevancy among the products comprises:
acquiring commodity classification structure information of each commodity;
calculating the commodity relevancy between any two commodities according to the classification structure information, wherein the specific formula is as follows:
wherein R isgoods(gi,gj)∈[0，1]；d(gi) Is a commodity giPath Length to root node, LCA (g)i,gj) Is a commodity giAnd goods gjThe common ancestor with the largest path.
5. The commodity recommendation method according to claim 4, wherein said calculating the score estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity score matrix of the relevant user and the commodity relevancy comprises:
screening the commodities with the product relevance larger than a preset value for the unscored commodities so as to obtain an unscored related commodity set of the unscored commodities and a corresponding unscored commodity relevance set;
calculating the grade estimation of the user to be recommended on the unscored commodities based on the relevant user commodity grade matrix and the unscored commodity relevance set, wherein the specific formula is as follows:
wherein, s (u)i,gj) For the user to be recommended to mark the unscored commodity gjThe score estimation of (2); rgoods(gk,gj) Is a commodity gkWith commercial product gjThe degree of correlation of (c); gsThe related commodities are collected.
6. The item recommendation method according to claim 5, wherein after calculating the score estimation of the unscored items to obtain a score estimation matrix of related items, the item recommendation method further comprises:
calculating the comprehensive scores of the user to be recommended on the commodities in the relevant commodity set according to the user similarity and the score estimation matrix of the relevant commodities, wherein the specific formula is as follows:
wherein,for the user u to be recommendediFor commodity gjThe composite score of (4); s (u)k,gj) For user ukTo-be-pushed commodity gjScoring of (4);for the user u to be recommendediA mean value of the scores for each of said commodities.
7. An article recommendation device, comprising:
the user similarity processing module is used for calculating the user similarity between the user to be recommended and each user according to the user tag information;
the user screening module is used for screening the user similarity result to obtain a related user set, a corresponding related user commodity scoring matrix and a related commodity set;
the commodity relevancy processing module is used for calculating the commodity relevancy among commodities in the relevant commodity set;
the score estimation module is used for calculating the score estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity score matrix of the relevant user and the commodity correlation degree so as to obtain the score estimation matrix of the relevant commodities;
and the recommendation processing module is used for sorting according to the grading result of each commodity and selecting a preset number of sorting results to recommend to the user to be recommended.
8. The article recommendation device according to claim 7, further comprising:
the comprehensive grading module is used for screening the commodities with the product relevance larger than a preset value for the unscored commodities to obtain an unscored related commodity set of the unscored commodities and a corresponding unscored commodity relevance set;
and calculating the comprehensive score of the user to be recommended on the unscored commodities based on the relevant user commodity scoring matrix and the unscored commodity relevance set.
9. A storage medium having stored thereon a computer program which, when executed by a processor, implements the item recommendation method according to any one of claims 1 to 6.
10. An electronic terminal, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the following via execution of the executable instructions:
calculating the user similarity between the user to be recommended and each user;
screening the user similarity result so as to obtain a relevant user set, a corresponding relevant user commodity scoring matrix and a relevant commodity set;
calculating commodity relevance among commodities in the relevant commodity set;
calculating the grade estimation of the unscored commodities of the user to be recommended in the relevant commodity set according to the commodity grade matrix of the relevant user and the commodity relevancy, and acquiring the grade estimation matrix of the relevant commodities;
and sorting according to the grading result of each commodity, and selecting a preset number of sorting results to send to the user to be recommended.
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