CN113407845A - Method and device for information recommendation, electronic equipment and storage medium - Google Patents
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Abstract
The application relates to the technical field of information recommendation, and discloses a method for information recommendation, which comprises the following steps: acquiring personal characteristic information of a user, label information of the user, context information of the user and social network information of the user; acquiring a first alternative recommendation list according to the personal characteristic information, acquiring a second alternative recommendation list according to the label information, acquiring a third alternative recommendation list according to the context information, and acquiring a fourth alternative recommendation list according to the social network information; respectively extracting information to be recommended from a first alternative recommendation list, a second alternative recommendation list, a third alternative recommendation list and a fourth alternative recommendation list according to a preset extraction rule, and merging the extracted information to be recommended to obtain a cold start recommendation list; and recommending the information to be recommended in the cold start recommendation list to the user. So as to improve the accuracy of recommending information to the new user. The application also discloses a device, electronic equipment and storage medium for information recommendation.
Description
Technical Field
The present application relates to the field of information recommendation technologies, and for example, to a method and an apparatus for information recommendation, an electronic device, and a storage medium.
Background
In the prior art, recommendation systems generally rely on historical behavior of users, such as: clicking, watching, language commenting, etc., to recommend information that may be of interest to the user, so the user's historical behavior data becomes an important component and prerequisite of the information recommendation system.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art: in the prior art, under the condition that the historical behavior data of the user is insufficient, the accuracy is poor when the information is recommended to the user.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
The embodiment of the disclosure provides a method and a device for information recommendation, electronic equipment and a storage medium, so as to improve the accuracy of information recommendation for a user.
In some embodiments, the method for information recommendation includes: acquiring personal characteristic information of a user, label information of the user, context information of the user and social network information of the user; acquiring a first alternative recommendation list according to the personal characteristic information, acquiring a second alternative recommendation list according to the label information, acquiring a third alternative recommendation list according to the context information, and acquiring a fourth alternative recommendation list according to the social network information; the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list and the fourth candidate recommendation list respectively comprise information recommended to a user; performing weighted calculation on the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list according to preset list weights to obtain a cold start recommendation list; recommending the cold start recommendation list to the user.
In some embodiments, the means for information recommendation comprises: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire personal characteristic information of a user, tag information of the user, context information of the user and social network information of the user; the second obtaining module is configured to obtain a first alternative recommendation list according to the personal feature information, obtain a second alternative recommendation list according to the tag information, obtain a third alternative recommendation list according to the context information, and obtain a fourth alternative recommendation list according to the social network information; the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list and the fourth candidate recommendation list respectively comprise information recommended to a user; the third obtaining module is configured to perform weighted calculation on the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list and the fourth candidate recommendation list according to preset list weights to obtain a cold start recommendation list; a recommending module configured to recommend the cold start recommendation list to the user.
In some embodiments, the electronic device comprises a processor and a memory storing program instructions, the processor being configured to perform the above-described method for information recommendation when executing the program instructions.
In some embodiments, the storage medium stores executable instructions that, when executed, perform the above-described method for information recommendation.
The method, the device, the electronic equipment and the storage medium for information recommendation provided by the embodiment of the disclosure can achieve the following technical effects: obtaining various information of personal characteristic information of a user, label information of the user, context information of the user and social network information of the user; respectively acquiring a first alternative recommendation list, a second alternative recommendation list, a third alternative recommendation list and a fourth alternative recommendation list corresponding to each piece of information; the alternative recommendation lists corresponding to different information are respectively obtained, and the alternative recommendation lists are combined according to the preset extraction rule to obtain the cold start recommendation list, so that the accuracy of recommending information for the user is improved.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:
FIG. 1 is a schematic diagram of a method for information recommendation provided by an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an apparatus for information recommendation provided by an embodiment of the present disclosure;
fig. 3 is a schematic diagram of another apparatus for information recommendation provided by an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure described herein may be made. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.
The term "plurality" means two or more unless otherwise specified.
In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
With reference to fig. 1, an embodiment of the present disclosure provides a method for information recommendation, including:
step S101, acquiring personal characteristic information of a user, label information of the user, context information of the user and social network information of the user;
step S102, a first alternative recommendation list is obtained according to the personal characteristic information, a second alternative recommendation list is obtained according to the label information, a third alternative recommendation list is obtained according to the context information, and a fourth alternative recommendation list is obtained according to the social network information; the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list respectively comprise information to be recommended;
step S103, extracting information to be recommended from a first alternative recommendation list, a second alternative recommendation list, a third alternative recommendation list and a fourth alternative recommendation list respectively according to a preset extraction rule, and merging the extracted information to be recommended to obtain a cold start recommendation list;
and step S104, recommending the information to be recommended in the cold start recommendation list to the user.
By adopting the method for information recommendation provided by the embodiment of the disclosure, various information such as personal characteristic information of a user, label information of the user, context information of the user and social network information of the user are obtained; respectively acquiring a first alternative recommendation list, a second alternative recommendation list, a third alternative recommendation list and a fourth alternative recommendation list corresponding to each piece of information; as the alternative recommendation lists corresponding to different information are respectively obtained and are combined according to the preset extraction rule to obtain the cold start recommendation list, the accuracy of recommending information to a new user is improved.
Optionally, obtaining the first candidate recommendation list according to the personal feature information includes: and performing collaborative filtering by using the personal characteristic information to obtain a first alternative recommendation list.
Optionally, the personal characteristic information of the user includes: age, sex, place of residence, etc. of the user.
Optionally, performing collaborative filtering by using the personal characteristic information to obtain a first candidate recommendation list, including: and performing collaborative filtering according to the personal characteristic information of the user, screening other users with the highest similarity with the personal characteristic information of the user, merging the recommendation information recommended to the other users, and obtaining a merged first alternative recommendation list.
Therefore, under the condition of lacking of historical behavior data and interest of the user, the information can be recommended to the user by acquiring the personal characteristic information of the user and utilizing the collaborative filtering algorithm to perform cold start recommendation on the user, and the accuracy of information recommendation on the user is improved.
Optionally, obtaining a second candidate recommendation list according to the tag information includes: screening recommendation information with label information from a preset first database; and sequencing the recommendation information with the label information according to the preset label priority to obtain a second alternative recommendation list.
Optionally, the tag information includes: sports, stars, movies, etc.
In some embodiments, the recommendation information with "sports", "star" and "movie" is filtered out from the preset first database; and sorting the screened recommendation information according to the label priority of 'sports > star > movie' to obtain a second alternative recommendation list.
Therefore, by acquiring the label information when the user registers and recommending the information for the user according to the label information, the information which accords with the user preference can be recommended to the user, and the accuracy of recommending the information for the user is improved.
Optionally, the context information includes season information, time information, region information, and picture information in the device of the user within a first preset time period; obtaining a third candidate recommendation list according to the context information, including: respectively acquiring a feature vector of each context information; carrying out weighted fusion on the feature vectors according to the preset information weight to obtain a first alternative feature vector; screening information to be recommended corresponding to a second candidate feature vector with the first similarity larger than a first preset threshold value from a preset second database; a plurality of second alternative characteristic vectors corresponding to the information to be recommended are stored in a second database; the first similarity is the similarity between the first candidate feature vector and each second candidate feature vector; and sorting the information to be recommended corresponding to the second candidate feature vectors with the first similarity larger than a first preset threshold value according to the sequence of the first similarity from high to low to obtain a third candidate recommendation list.
Optionally, the obtaining the feature vector of each context information respectively includes: and respectively acquiring the feature vectors of the seasonal information, the time information, the region information and the picture information.
Therefore, by acquiring the context information of the user, the user can be recommended accurately according to different context information. For example: and recommending the 'playing of the North sea park' to the user under the conditions that the season information of the user is spring, the time information is { May, holidays }, the region information is { Beijing, Western district }, and the picture information is a park. Therefore, the information suitable for the preference of the user can be recommended to the user by recommending the user by combining different seasons, different times, different regions and different picture information, so that the accuracy of information recommendation of the user is improved.
Optionally, the social network information includes a chat text and an association graph structure of the user in a preset time period, and the obtaining of the fourth candidate recommendation list according to the social network information includes: extracting text information from the chat text, and extracting graph structure characteristic information from the association graph structure by using a graph neural network; and acquiring a fourth alternative recommendation list according to the text information and the graph structure feature information.
Optionally, obtaining a fourth alternative recommendation list according to the text information and the graph structure feature information includes: respectively obtaining feature vectors of text information and graph structure feature information, carrying out vector splicing on the feature vectors of the text information and the feature vectors of the graph structure feature information to obtain a third candidate feature vector, and screening information to be recommended corresponding to a fourth candidate feature vector with a second similarity larger than a second preset threshold value from a preset third database; a plurality of fourth alternative characteristic vectors corresponding to the information to be recommended are stored in the second database; the second similarity is the similarity between the third candidate feature vector and each fourth candidate feature vector; and sorting the information to be recommended corresponding to the fourth candidate feature vector with the second similarity larger than a second preset threshold value according to the sequence of the second similarity from high to low to obtain a fourth candidate recommendation list.
Optionally, feature vectors of the Text information are obtained through TextCNN (Text conditional Neural Networks, Text classification algorithm based on Convolutional Neural Networks).
Optionally, the feature vector of the Graph structure feature information is obtained through a GCN (Graph neural Network).
Alternatively, the association graph structure of the user is constructed by acquiring people who the user communicates with and chats about in a preset time period, group chatting in which the user participates in the preset time period, the group chatting about and the frequency of participating in the group chatting, and the like.
In this way, the social network information of the user is obtained, natural language processing is carried out on the chat text, text information can be extracted from the chat text, and graph structure feature information is extracted from the associated graph structure by utilizing a graph neural network through a deep learning technology; the information recommendation method and the information recommendation device have the advantages that the information recommendation is performed on the user according to the text information and the graph structure characteristic information, and the information suitable for the preference of the user can be recommended to the user, so that the accuracy of information recommendation on the user is improved.
Optionally, extracting information to be recommended from the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list, and the fourth candidate recommendation list according to a preset extraction rule, and merging the extracted information to be recommended to obtain a cold start recommendation list, including: and respectively extracting information to be recommended from the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list according to a preset extraction ratio, and merging the extracted information to be recommended to obtain a cold start recommendation list.
In some embodiments, if the extraction ratios of the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list and the fourth candidate recommendation list are all 100%, the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list and the fourth candidate recommendation list are directly merged to obtain the cold-start recommendation list.
In some embodiments, if the extraction proportion of the first candidate recommendation list is 20%, extracting information to be recommended, which is ranked 20% first, from the first candidate recommendation list; if the extraction proportion of the second alternative recommendation list is 40%, extracting the information to be recommended which is 40% of the information to be recommended from the second alternative recommendation list; if the extraction proportion of the third alternative recommendation list is 30%, extracting the information to be recommended which is 30% of the information to be recommended from the third alternative recommendation list; if the extraction proportion of the fourth alternative recommendation list is 10%, extracting the information to be recommended which is 10% of the top in the sequence from the fourth alternative recommendation list; and combining the extracted information to be recommended to obtain a cold start recommendation list.
The alternative recommendation lists corresponding to different information are respectively obtained, the alternative recommendation lists are combined according to the preset extraction rule to obtain the cold start recommendation list, and the information to be recommended in the cold start recommendation list is recommended to the user, so that the accuracy of recommending information to a new user is improved, and meanwhile, the information recommendation with higher accuracy is realized to the user in the recommendation technical field.
As shown in fig. 2, an embodiment of the present disclosure provides an apparatus for information recommendation, including: a first obtaining module 201, a second obtaining module 202, a third obtaining module 203 and a recommending module 204; the first obtaining module 201 is configured to obtain personal characteristic information of a user, tag information of the user, context information of the user and social network information of the user, and send the personal characteristic information of the user, the tag information of the user, the context information of the user and the social network information of the user to the second obtaining module; the second obtaining module 202 is configured to receive the personal feature information of the user, the tag information of the user, the context information where the user is located, and the social network information of the user, which are sent by the first obtaining module, obtain the first candidate recommendation list according to the personal feature information, obtain the second candidate recommendation list according to the tag information, obtain the third candidate recommendation list according to the context information, and obtain the fourth candidate recommendation list according to the social network information; the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list respectively comprise information to be recommended; the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list are sent to a third acquisition module; the third obtaining module 203 is configured to receive the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list and the fourth candidate recommendation list sent by the second obtaining module, respectively extract information to be recommended from the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list and the fourth candidate recommendation list according to a preset extraction rule, combine the extracted information to be recommended to obtain a cold start recommendation list, and send the cold start recommendation list to the recommending module; the recommending module 204 is configured to receive the cold-start recommendation list sent by the third obtaining module, and recommend information to be recommended in the cold-start recommendation list to the user.
By adopting the device for information recommendation provided by the embodiment of the disclosure, the personal characteristic information of the user, the label information of the user, the context information of the user and the social network information of the user are acquired through the first acquisition module; the second obtaining module obtains a first alternative recommendation list according to the personal characteristic information, obtains a second alternative recommendation list according to the label information, obtains a third alternative recommendation list according to the context information, and obtains a fourth alternative recommendation list according to the social network information; the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list respectively comprise information to be recommended; the third acquisition module respectively extracts information to be recommended from the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list according to a preset extraction rule, and combines the extracted information to be recommended to obtain a cold start recommendation list; and the recommending module recommends the information to be recommended in the cold-start recommending list to the user. The alternative recommendation lists corresponding to different information are respectively obtained, and the alternative recommendation lists are combined according to the preset extraction rule to obtain the cold start recommendation list, so that the accuracy of recommending information for the user is improved.
Optionally, the second obtaining module is configured to obtain the first candidate recommendation list according to the personal feature information by: and performing collaborative filtering by using the personal characteristic information to obtain a first alternative recommendation list.
Optionally, the second obtaining module is configured to obtain the second candidate recommendation list according to the tag information by: screening recommendation information with label information from a preset first database; and sequencing the recommendation information with the label information according to the preset label priority to obtain a second alternative recommendation list.
Optionally, the second obtaining module is configured to obtain the third candidate recommendation list according to the context information by: respectively acquiring a feature vector of each context information; carrying out weighted fusion on the feature vectors according to the preset information weight to obtain a first alternative feature vector; screening information to be recommended corresponding to a second candidate feature vector with the first similarity larger than a first preset threshold value from a preset second database; a plurality of second alternative characteristic vectors corresponding to the information to be recommended are stored in a second database; the first similarity is the similarity between the first candidate feature vector and each second candidate feature vector; and sorting the information to be recommended corresponding to the second candidate feature vectors with the first similarity larger than a first preset threshold value according to the sequence of the first similarity from high to low to obtain a third candidate recommendation list.
Optionally, the social network information includes a chat text and an association graph structure of the user within a preset time period, and the second obtaining module is configured to obtain the fourth alternative recommendation list according to the social network information by: extracting text information from the chat text, and extracting graph structure characteristic information from the association graph structure by using a graph neural network; and acquiring a fourth alternative recommendation list according to the text information and the graph structure feature information.
Optionally, the third obtaining module is configured to extract information to be recommended from the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list, and the fourth candidate recommendation list according to a preset extraction rule in the following manner, and merge the extracted information to be recommended to obtain a cold start recommendation list, including: and respectively extracting information to be recommended from the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list according to a preset extraction ratio, and merging the extracted information to be recommended to obtain a cold start recommendation list.
As shown in fig. 3, an apparatus for information recommendation according to an embodiment of the present disclosure includes a processor (processor)300 and a memory (memory)301 storing program instructions. Optionally, the electronic device may further include a Communication Interface (Communication Interface)302 and a bus 303. The processor 300, the communication interface 302 and the memory 301 may communicate with each other via a bus 303. The communication interface 302 may be used for information transfer. The processor 300 may call program instructions in the memory 301 to perform the method for information recommendation of the above-described embodiment.
By adopting the device for information recommendation provided by the embodiment of the disclosure, various information such as personal characteristic information of a user, label information of the user, context information of the user and social network information of the user are obtained; respectively acquiring a first alternative recommendation list, a second alternative recommendation list, a third alternative recommendation list and a fourth alternative recommendation list corresponding to each piece of information; the alternative recommendation lists corresponding to different information are respectively obtained, and the alternative recommendation lists are combined according to the preset extraction rule to obtain the cold start recommendation list, so that the accuracy of recommending information for the user is improved.
In addition, the program instructions in the memory 301 may be implemented in the form of software functional units and stored in a readable storage medium when the program instructions are sold or used as independent products.
The memory 301 is a readable storage medium and can be used for storing software programs, executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 300 executes functional applications and data processing, i.e. implements the method for information recommendation in the above-described embodiments, by executing program instructions/modules stored in the memory 301.
The memory 301 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. Further, the memory 301 may include a high-speed random access memory, and may also include a nonvolatile memory.
The embodiment of the disclosure provides an electronic device, which includes the above device for information recommendation.
By adopting the electronic equipment provided by the embodiment of the disclosure, various information such as personal characteristic information of a user, label information of the user, context information of the user and social network information of the user are obtained; respectively acquiring a first alternative recommendation list, a second alternative recommendation list, a third alternative recommendation list and a fourth alternative recommendation list corresponding to each piece of information; the alternative recommendation lists corresponding to different information are respectively obtained, and the alternative recommendation lists are combined according to the preset extraction rule to obtain the cold start recommendation list, so that the accuracy of recommending information for the user is improved.
Alternatively, the electronic device is a computer or a server, etc.
The embodiment of the disclosure provides a storage medium, which stores executable instructions configured to execute the method for information recommendation.
The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described method for information recommendation.
The readable storage medium may be a transitory readable storage medium or a non-transitory readable storage medium.
The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. 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, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method for information recommendation, comprising:
acquiring personal characteristic information of a user, label information of the user, context information of the user and social network information of the user;
acquiring a first alternative recommendation list according to the personal characteristic information, acquiring a second alternative recommendation list according to the label information, acquiring a third alternative recommendation list according to the context information, and acquiring a fourth alternative recommendation list according to the social network information; the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list respectively comprise information to be recommended;
extracting information to be recommended from the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list respectively according to a preset extraction rule, and merging the extracted information to be recommended to obtain a cold start recommendation list;
and recommending the information to be recommended in the cold start recommendation list to the user.
2. The method of claim 1, wherein obtaining a first list of alternative recommendations based on the personal trait information comprises:
and performing collaborative filtering by using the personal characteristic information to obtain a first alternative recommendation list.
3. The method of claim 1, wherein obtaining a second alternative recommendation list according to the tag information comprises:
screening recommendation information with the label information from a preset first database;
and sequencing the recommendation information with the label information according to a preset label priority to obtain a second alternative recommendation list.
4. The method of claim 1, wherein the context information comprises seasonal information, temporal information, regional information, and picture information within the user's device during a first preset time period; obtaining a third candidate recommendation list according to the context information, including:
respectively acquiring a feature vector of each piece of context information;
performing weighted fusion on the feature vectors according to preset information weight to obtain a first alternative feature vector;
screening information to be recommended corresponding to a second candidate feature vector with the first similarity larger than a first preset threshold value from a preset second database; a plurality of second alternative characteristic vectors corresponding to the information to be recommended are stored in the second database; the first similarity is the similarity between the first candidate feature vector and each second candidate feature vector;
and sorting the information to be recommended corresponding to the second candidate feature vectors with the first similarity larger than a first preset threshold value according to the sequence of the first similarity from high to low to obtain a third candidate recommendation list.
5. The method of claim 1, wherein the social network information includes a chat text and an association graph structure of the user in a preset time period, and obtaining a fourth alternative recommendation list according to the social network information includes:
extracting text information from the chat text, and extracting graph structure feature information from the associated graph structure by using a graph neural network;
and acquiring a fourth alternative recommendation list according to the text information and the graph structure feature information.
6. The method according to any one of claims 1 to 5, wherein information to be recommended is respectively extracted from the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list, and the fourth candidate recommendation list according to a preset extraction rule, and the extracted information to be recommended is merged to obtain a cold start recommendation list, including:
and respectively extracting information to be recommended from the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list according to a preset extraction ratio, and merging the extracted information to be recommended to obtain a cold start recommendation list.
7. An apparatus for information recommendation, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire personal characteristic information of a user, tag information of the user, context information of the user and social network information of the user;
the second obtaining module is configured to obtain a first alternative recommendation list according to the personal feature information, obtain a second alternative recommendation list according to the tag information, obtain a third alternative recommendation list according to the context information, and obtain a fourth alternative recommendation list according to the social network information; the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list respectively comprise information to be recommended;
the third obtaining module is configured to respectively extract information to be recommended from the first candidate recommendation list, the second candidate recommendation list, the third candidate recommendation list and the fourth candidate recommendation list according to a preset extraction rule, and combine the extracted information to be recommended to obtain a cold start recommendation list;
and the recommending module is configured to recommend the information to be recommended in the cold-start recommending list to the user.
8. An apparatus for information recommendation, comprising a processor and a memory storing program instructions, characterized in that the processor is configured to perform the method of any of claims 1 to 6 when executing the program instructions.
9. An electronic device, characterized in that it comprises an apparatus for information recommendation according to claim 8.
10. A storage medium storing program instructions, characterized in that said program instructions, when executed, perform a method for information recommendation according to any of claims 1 to 6.
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