CN112561581A - Recommendation method and device, electronic equipment and storage medium - Google Patents
Recommendation method and device, electronic equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
- G06Q30/0256—User search
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0269—Targeted advertisements based on user profile or attribute
- G06Q30/0271—Personalized advertisement
Abstract
The application provides a recommendation method, a recommendation device, an electronic device and a storage medium, wherein the recommendation method comprises the following steps: acquiring data information of a user; determining a first recommended commodity based on the data information and a knowledge graph relationship model; determining a second recommended commodity based on the data information and a case map relation model; determining a third recommended commodity with semantic similarity larger than a similarity threshold value with the data information; recommending for the user based on the first recommended commodity, the second recommended commodity and the third recommended commodity.
Description
Technical Field
The present application relates to the field of electronic commerce technologies, and in particular, to a recommendation method, an apparatus, an electronic device, and a storage medium.
Background
The recommendation system is used on the home platform of each large e-commerce platform. The current recommendation system mainly has the following methods: recommendations based on user statistics; recommending based on the product attribute characteristics; recommending through collaborative filtering; recommending according to the product in which the user is interested, and the like. The methods have some own disadvantages, and the recommendation systems are single and not perfect enough, so that the recommended commodities are not comprehensive.
Disclosure of Invention
In order to solve the above problems, the present application provides a recommendation method, apparatus, electronic device, and storage medium.
The application provides a recommendation method, which comprises the following steps:
acquiring data information of a user;
determining a first recommended commodity based on the data information and a knowledge graph relationship model;
determining a second recommended commodity based on the data information and a case map relation model;
determining a third recommended commodity with semantic similarity larger than a similarity threshold value with the data information;
recommending for the user based on the first recommended commodity, the second recommended commodity and the third recommended commodity.
In some embodiments, the determining a first recommended good based on the data information and a knowledge-graph relationship model includes:
determining attribute information of the commodity based on the data information;
and determining first recommended commodity information from the knowledge graph based on the attribute information of the commodity.
In some embodiments, the data information includes keywords of the item, and the determining a second recommended item based on the data information and the case graph relationship model includes:
determining first target commodity information based on the keywords of the commodity;
and determining a second recommended commodity based on the first target commodity information and the affair map relation model.
In some embodiments, the determining a second recommended good based on the first target good information and the case graph relationship model includes:
determining semantic similarity between the first target commodity information and the commodity information in the event graph based on the first target commodity information;
determining second target commodity information with the semantic similarity larger than a similarity threshold value with the first target commodity information based on the semantic similarity;
determining the matter relation of the second target commodity information in the matter map relation model;
and determining the second recommended commodity based on the affair relationship.
In some embodiments, the recommending a user based on the first recommended commodity, the second recommended commodity, and the third recommended commodity includes:
determining weight information of each of the first recommended commodity, the second recommended commodity and the third recommended commodity;
determining a recommendation order based on the weight information of each commodity;
and recommending the user based on the recommendation ranking.
In some embodiments, the weight information of the first recommended item is greater than the weight information of the second recommended item and/or the weight information of the third recommended item.
In some embodiments, the method further comprises:
acquiring purchasing data information and browsing information of a user, and acquiring commodity information;
determining knowledge-graph data of a user based on the purchase data information and the browsing information;
determining knowledge-graph data of the commodity based on the commodity information;
and constructing a knowledge graph relation model based on the knowledge graph data of the user and the knowledge graph data of the commodity.
An embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the electronic device executes any one of the recommendation methods described above.
An embodiment of the present application provides a storage medium, where a computer-executable instruction is stored in the storage medium, and the computer-executable instruction is configured to execute any one of the recommendation methods described above.
According to the recommendation method, the recommendation device, the electronic equipment and the storage medium, when data information of a user is obtained, a first recommended commodity for the user is determined based on a knowledge graph relation model; determining a second recommended commodity recommended for the user based on the event graph relation model; determining a third recommended commodity recommended for the user based on the semantic relation; and finally, recommending the first recommended commodity, the second recommended commodity and the third recommended commodity for the user.
Drawings
The present application will be described in more detail below on the basis of embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart illustrating an implementation of a recommendation method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a process of determining a second recommended product based on the first target product information and the event graph relationship model according to the embodiment of the present application;
fig. 3 is a schematic flow chart illustrating an implementation of a recommendation method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a recommendation device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
In the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
The following description will be added if a similar description of "first \ second \ third" appears in the application file, and in the following description, the terms "first \ second \ third" merely distinguish similar objects and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may be interchanged under certain circumstances in a specific order or sequence, so that the embodiments of the application described herein can be implemented in an order other than that shown or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
The embodiment of the application provides a recommendation method, and the method is applied to electronic equipment. The electronic device may be a mobile terminal, a computer, or the like, and the functions implemented by the recommendation method provided in the embodiments of the present application may be implemented by a processor of the electronic device calling a program code, where the program code may be stored in a computer storage medium. An embodiment of the present application provides a recommendation method, and fig. 1 is a schematic flow chart illustrating an implementation of the recommendation method provided in the embodiment of the present application, and as shown in fig. 1, the method includes:
step S101, data information of the user is obtained.
In this embodiment, the data information may be search information input by a user, may also be a purchase record, a browsing record, and the like of the user, and in some embodiments, may also be favorite data of the user, a user habit, and the like.
And S102, determining a first recommended commodity based on the data information and the knowledge graph relation model.
In the embodiment of the application, the knowledge graph relation model is pre-stored in the electronic equipment, and the electronic equipment can acquire knowledge graph data of a user and knowledge graph data of a commodity and construct the knowledge graph relation model based on the knowledge graph data of the user and the knowledge graph data of the commodity. In the embodiment of the application, matching search can be performed in the knowledge graph relation model based on the data information of the user, so that the first recommended commodity is determined. Illustratively, the data information is 'beef balls', and a first recommended commodity can be determined through a knowledge graph relation model, wherein the first recommended commodity can comprise a plurality of commodities, and the correlation degree of the commodities to the beef balls is greater than a first preset threshold value.
And step S103, determining a second recommended commodity based on the data information and the affair map relation model.
In the embodiment of the application, the case map relationship model is pre-stored in the electronic device, the electronic device can acquire the well-defined case map relationship model, so that the second recommended commodity is determined based on the case map relationship model, the case map relationship model comprises the relationship between events, the entity can be a commodity, and after each event in the case map occurs, the entity can be connected with the subsequent events. For example: the data information is beef balls, follow-up chafing dish is determined according to the relation model of the matter atlas, food materials related to the chafing dish can be determined based on the matter atlas, in the embodiment of the application, after an event occurs, the follow-up event corresponds to different weights, and a second recommended commodity can be determined based on the weight information. For example, several items having a larger weight may be selected as the second recommended items.
And step S104, determining a third recommended commodity of which the semantic similarity with the data information is greater than a similarity threshold.
In the embodiment of the application, the data information can be converted into the first vector through the semantic relationship, each commodity is also converted into the corresponding vector set, the vector set comprises the second vectors of a plurality of commodities, the semantic similarity between the first vector and the second vector can be calculated, and the commodity corresponding to the second vector with the semantic similarity larger than the similarity threshold is determined as the third recommended commodity. For example, the data information is beef balls, and the commodities such as beef, balls and the like are determined to be the third recommended commodity through the semantic relation. In the embodiment of the application, the similarity threshold is smaller than a first preset threshold.
Step S105, recommending the user based on the first recommended commodity, the second recommended commodity and the third recommended commodity.
In the embodiment of the application, the ranks of the first recommended commodity, the second recommended commodity and the third recommended commodity can be determined according to the weight relationship, and then recommendation is performed for the user based on the rank information.
According to the recommendation method, the recommendation device, the electronic equipment and the storage medium, when data information of a user is obtained, a first recommended commodity for the user is determined based on a knowledge graph relation model; determining a second recommended commodity recommended for the user based on the event graph relation model; determining a third recommended commodity recommended for the user based on the semantic relation; and finally, recommending the first recommended commodity, the second recommended commodity and the third recommended commodity for the user.
In some embodiments, the step S102 "determining the first recommended goods based on the data information and the knowledge-graph relationship model" may be implemented by:
step S1, the attribute information of the commodity is determined based on the data information.
In this embodiment, the attribute information of the product may be a name of the product, for example, the data information may be a keyword, and a correlation between the data information and the name is determined based on the keyword, so that the attribute information of the product is determined based on the correlation.
Step S2, determining first recommended commodity information from the knowledge graph based on the attribute information of the commodity.
In the embodiment of the application, after the attribute information of the commodity is determined, a first recommended commodity can be determined based on the knowledge graph, and the example above is taken, and the commodity corresponding to the keyword with the relevancy being greater than a first preset threshold is determined as the first recommended commodity.
According to the recommendation method provided by the embodiment of the application, accurate recommendation can be performed on the user through the data information and knowledge graph relation model.
In some embodiments, the data information includes a keyword, and the step S103 "determining a second recommended product based on the data information and the case graph relationship model" may be implemented by:
step S11, based on the keyword, determines first target product information.
Step S12, determining a second recommended commodity based on the first target commodity information and the case map relationship model.
In the embodiment of the application, the case-of-affair map relation model includes a relation between time and time, illustratively, the first target commodity information is beef king, the case-of-affair map relation includes a relation between beef balls and related chafing dish commodities, and at this time, it can be determined that the second recommended commodity is related chafing dish commodities.
In some embodiments, the step S12 "determining a second recommended commodity based on the first target commodity information and the case-map relationship model" may be implemented by the following steps, and fig. 2 is a schematic flow chart of determining a second recommended commodity based on the first target commodity information and the case-map relationship model, as shown in fig. 2, and includes:
step S21, determining semantic similarity between the first target commodity information and the commodity information in the affair map relation model.
Step S22, based on the semantic similarity, determining second target product information whose semantic similarity with the first target product information is greater than a similarity threshold.
In this embodiment, the similarity threshold may be preset.
Step S23, determining a case relationship of the second target product information in the case relationship graph relationship model.
In the embodiment of the application, the fact relation corresponding to the second target commodity information can be determined based on the fact map relation model.
Step S24, determining the second recommended item based on the case relationship.
Through the matter relation, the commodity with high relevance can be determined to be the second recommended commodity.
In some embodiments, the step S105 "recommending the user based on the first recommended item, the second recommended item, and the third recommended item" may be implemented by:
in step S31, weight information of each of the first recommended item, the second recommended item, and the third recommended item is determined.
In this embodiment of the application, the weight information of each product may be preset, for example, the weight information of the first recommended product is greater than the weight information of the second recommended product and/or the weight information of the third recommended product.
In step S32, a recommendation order is determined based on the weight information of each product.
And step S33, recommending the user based on the recommendation sequence.
In the embodiment of the application, commodities with the highest recommendation ranking can be selected for recommendation for the user.
In some embodiments, before step S102, the method further comprises:
step S41, acquires the purchase data information and browsing information of the user, and acquires the commodity information.
In the embodiment of the application, the purchasing data information and the browsing information of the user can be directly obtained from the database of the shopping website, and meanwhile, the commodity information can be obtained from the database.
Step S42, determining knowledge-graph data of the user based on the purchase data information and the browsing information.
In the embodiment of the application, the knowledge graph data of the user comprises the attributes, preferences, habits and the like of the user.
Step S43, determining knowledge map data of the commodity based on the commodity information.
In the embodiment of the application, the knowledge graph data of the commodity comprises the name of the commodity and the like.
Step S44, constructing the knowledge-graph relation model based on the knowledge-graph data of the user and the knowledge-graph data of the commodity.
Based on the foregoing embodiments, a recommendation method is further provided in an embodiment of the present application, and fig. 3 is a schematic flow chart illustrating an implementation of the recommendation method provided in the embodiment of the present application, as shown in fig. 3, the method includes:
in step S51, a knowledge graph (similar to the knowledge graph relation model in the above embodiment) is created.
At the beginning, a knowledge graph of the commodity and the user is constructed, wherein the main content of the knowledge graph comprises information such as the name and the attribute of the commodity, some attributes and information of the user, and most importantly, purchase information of the user. The user can be recommended better on the basis of the information. The information of the commodity can be obtained from the inside of the company or from the internet, the more the data is, the more detailed the constructed map is, and the better the effect is when the map is applied to the following recommendation. The user's knowledge-graph data is obtained from the user's information.
And step S52, carrying out accurate search and semantic search.
In the embodiment of the application, after the knowledge graph is constructed, the search information of a user is firstly acquired, and the relevant attributes of the commodity are extracted. And then, using accurate search to find out the most accurate information for the user, and if the accurate information is not found, finding out related commodities for the user according to semantic correlation.
Step S53, the purchasing and browsing behavior of the user is then recorded in the knowledge-graph.
And step S54, recommending the user according to the knowledge graph, the affair graph and the semantic relation.
When the user opens the system again, new recommendations are made for the user according to the purchases, and the recommendations are not only the same commodity recommendations but also event-related recommendations. For example, when a user searches or purchases "beef balls", related commodities, such as beef, can be recommended for the user according to the attributes of the beef balls, and also according to a case (similar to the case map relationship model in the above embodiment), for example, after purchasing the beef balls, the user may be going to do hot pot eating by himself, so that food materials and contents related to the hot pot are recommended for the user next. And moreover, combined recommendation is carried out according to the semantics, and commodities with similar semantics with the beef ball are recommended to the user, such as beef, balls and the like. Therefore, commodities can be well recommended for the user from knowledge maps, affair maps and semantic similarity.
The recommendation method provided by the embodiment of the application is perfected according to the defects of the existing recommendation system, and better recommends for users.
Based on the foregoing embodiments, the present application provides a recommendation apparatus, where the modules included in the apparatus and the units included in the modules may be implemented by a processor in a computer device; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the processor may be a Central Processing Unit (CPU), a Microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.
An embodiment of the present application provides a recommendation device, fig. 4 is a schematic structural diagram of the recommendation device provided in the embodiment of the present application, and as shown in fig. 4, the recommendation device 400 includes:
a first obtaining module 401, configured to obtain data information of a user;
a first determining module 402, configured to determine a first recommended product based on the data information and a knowledge-graph relationship model;
a second determining module 403, configured to determine a second recommended product based on the data information and the case map relationship model;
a third determining module 404, configured to determine a third recommended product of which semantic similarity with the data information is greater than a similarity threshold;
a recommending module 405, configured to recommend to a user based on the first recommended product, the second recommended product, and the third recommended product.
In some embodiments, the first determining module 402 includes:
a first determination unit configured to determine attribute information of the commodity based on the data information;
a second determination unit configured to determine first recommended commodity information from the knowledge graph based on the attribute information of the commodity.
The data information includes a keyword, and the second determining module 403 includes:
a third determining unit configured to determine the first target commodity information based on the keyword;
a fourth determining unit, configured to determine a second recommended product based on the first target product information and the case map relationship model.
In some embodiments, the fourth determining unit includes:
the first determining subunit is used for determining semantic similarity between the first target commodity information and the commodity information in the event graph relation model based on the first target commodity information;
the second determining subunit is used for determining second target commodity information, of which the semantic similarity with the first target commodity information is greater than a similarity threshold value, based on the semantic similarity;
the third determining subunit is used for determining the matter relation of the second target commodity information in the matter map relation model;
and the fourth determining subunit is used for determining the second recommended commodity based on the affair relationship.
In some embodiments, recommendation module 405, includes:
a fifth determining unit configured to determine weight information of each of the first recommended item, the second recommended item, and the third recommended item;
a sixth determining unit configured to determine a recommendation ranking based on the weight information of each commodity;
and the seventh determining unit is used for recommending the user based on the recommendation sequence.
In some embodiments, the weight information of the first recommended item is greater than the weight information of the second recommended item and/or the weight information of the third recommended item.
In some embodiments, the recommendation device 400 further comprises:
the second acquisition module is used for acquiring the purchase data information and the browsing information of the user and acquiring the commodity information;
a fourth determination module to determine knowledge-graph data of the user based on the purchase data information and the browsing information;
a fifth determining module, configured to determine knowledge graph data of the commodity based on the commodity information;
and the construction module is used for constructing the knowledge graph relation model based on the knowledge graph data of the user and the knowledge graph data of the commodity.
It should be noted that, in the embodiment of the present application, if the recommendation method is implemented in the form of a software functional module and sold or used as a standalone product, the recommendation method may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, an embodiment of the present application provides a storage medium, on which a computer program is stored, wherein the computer program is executed by a processor to implement the steps in the recommendation method provided in the above embodiment.
The embodiment of the application provides an electronic device; fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 5, the electronic device 500 includes: a processor 501, at least one communication bus 502, a user interface 503, at least one external communication interface 504, and a memory 505. Wherein the communication bus 502 is configured to enable connective communication between these components. The user interface 503 may include a display screen, and the external communication interface 504 may include a standard wired interface and a wireless interface, among others. The processor 501 is configured to execute the program of the recommendation method stored in the memory to implement the steps in the recommendation method provided in the above embodiments.
The above description of the display device and storage medium embodiments is similar to the description of the method embodiments above, with similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the computer device and the storage medium of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
Here, it should be noted that: the above description of the storage medium and device embodiments is similar to the description of the method embodiments above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and apparatus of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A recommendation method, comprising:
acquiring data information of a user;
determining a first recommended commodity based on the data information and a knowledge graph relationship model;
determining a second recommended commodity based on the data information and a case map relation model;
determining a third recommended commodity with semantic similarity larger than a similarity threshold value with the data information;
recommending for the user based on the first recommended commodity, the second recommended commodity and the third recommended commodity.
2. The method of claim 1, the determining a first recommended good based on the data information and a knowledge-graph relationship model, comprising:
determining attribute information of the commodity based on the data information;
and determining first recommended commodity information from the knowledge graph based on the attribute information of the commodity.
3. The method of claim 1, wherein the data information comprises keywords, and wherein determining a second recommended good based on the data information and a physics-graph relationship model comprises:
determining first target commodity information based on the keywords;
and determining a second recommended commodity based on the first target commodity information and the affair map relation model.
4. The method of claim 3, wherein the determining a second recommended good based on the first target good information and the case graph relationship model comprises:
determining semantic similarity between the first target commodity information and the commodity information in the event graph relation model;
determining second target commodity information with the semantic similarity larger than a similarity threshold value with the first target commodity information based on the semantic similarity;
determining the matter relation of the second target commodity information in the matter map relation model;
and determining the second recommended commodity based on the affair relationship.
5. The method of claim 1, wherein recommending for the user based on the first recommended item, the second recommended item, and the third recommended item comprises:
determining weight information of each of the first recommended commodity, the second recommended commodity and the third recommended commodity;
determining a recommendation order based on the weight information of each commodity;
and recommending the user based on the recommendation ranking.
6. The method according to claim 5, wherein the weight information of the first recommended item is larger than the weight information of the second recommended item and/or the weight information of the third recommended item.
7. The method of claim 1, further comprising:
acquiring purchasing data information and browsing information of a user, and acquiring commodity information;
determining knowledge-graph data of a user based on the purchase data information and the browsing information;
determining knowledge-graph data of the commodity based on the commodity information;
and constructing the knowledge graph relation model based on the knowledge graph data of the user and the knowledge graph data of the commodity.
8. A recommendation device, comprising:
the first acquisition module is used for acquiring data information of a user;
the first determination module is used for determining a first recommended commodity based on the data information and the knowledge graph relation model;
the second determination module is used for determining a second recommended commodity based on the data information and the affair map relation model;
the third determining module is used for determining a third recommended commodity of which the semantic similarity with the data information is greater than a similarity threshold;
and the recommending module is used for recommending the user based on the first recommended commodity, the second recommended commodity and the third recommended commodity.
9. An electronic device, comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the recommendation method of any one of claims 1 to 7.
10. A storage medium having stored thereon computer-executable instructions for performing the recommendation method of any one of claims 1 to 7.
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