CN111209477B - Information recommendation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the application provides an information recommendation method and device, electronic equipment and storage medium, wherein the method comprises the following steps: when a user request is received, acquiring a user side characteristic corresponding to the user request; generating a plurality of user side sub-vectors corresponding to the user side characteristics by adopting a preset user side network, wherein each user side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight; the recall target weight is adopted, and the plurality of user side sub-vectors are formed into a user side vector; searching target information matched with the user side vector from a preset information database, and recommending the target information to a user. According to the embodiment of the application, information recommendation combining a plurality of recall targets is realized, the recall targets can be timely adjusted during service steering, and resource waste caused by retraining is avoided.
Description
Technical Field
The present invention relates to the field of information recommendation, and in particular, to a method and apparatus for information recommendation, an electronic device, and a storage medium.
Background
With the development of internet technology, information resources are in explosive growth, and personalized recommendation becomes an indispensable basic technology in news, short videos, music and other products.
Taking a short video scene as an example, calculating the similarity between a user and the short video, and fitting a single recall target with the similarity as much as possible to obtain a recommended video, wherein the recall targets are usually different in different service periods, when the recall targets are changed, the user needs to retrain according to the new recall targets, a large amount of resources are consumed, and the recall targets are difficult to adjust in time, so that the short board for service diversion is formed.
Disclosure of Invention
In view of the foregoing, a method and apparatus, an electronic device, a storage medium, and a computer program product are provided for providing an information recommendation that overcomes or at least partially solves the foregoing, including:
a method of information recommendation, the method comprising:
when a user request is received, acquiring a user side characteristic corresponding to the user request;
generating a plurality of user side sub-vectors corresponding to the user side characteristics by adopting a preset user side network, wherein each user side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight;
the recall target weight is adopted, and the plurality of user side sub-vectors are formed into a user side vector;
searching target information matched with the user side vector from a preset information database, and recommending the target information to a user.
An apparatus for information recommendation, the apparatus comprising:
the user side characteristic acquisition module is used for acquiring user side characteristics corresponding to a user request when the user request is received;
the user side sub-vector generation module is used for generating a plurality of user side sub-vectors corresponding to the user side characteristics by adopting a preset user side network, each user side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight;
the user side vector composition module is used for composing the plurality of user side sub-vectors into a user side vector by adopting the recall target weight;
and the information recommending module is used for searching target information matched with the user side vector from a preset information database and recommending the target information to a user.
An electronic device comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, which when executed by the processor, performs the steps of the method of information recommendation as described above.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a method of information recommendation as described above.
The embodiment of the application has the following advantages:
in the embodiment of the application, when a user request is received, the user side characteristics corresponding to the user request are acquired, a preset user side network is adopted, a plurality of user side sub-vectors corresponding to the user side characteristics are generated, each user side sub-vector corresponds to a recall target, each recall target corresponds to a recall target weight, then the recall target weights are adopted, the plurality of user side sub-vectors form a user side vector, target information matched with the user side vectors is searched from a preset information database, and the target information is recommended to a user, so that information recommendation combined with the plurality of recall targets is realized, the recall targets can be timely adjusted during service diversion, and resource waste is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the description of the present application will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of steps of a method for recommending information according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a system for recommending information according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a network for information recommendation according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an apparatus for information recommendation according to an embodiment of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. It will be apparent that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Referring to fig. 1, a flowchart illustrating steps of a method for recommending information according to an embodiment of the present application may specifically include the following steps:
as an example, the user-side features may include any one or more of the following:
user attribute features, information preference features.
In news, short video, music, etc., a client may generate a User request and send the User request to a server in response to a User (User) operation, such as turning on a short video client.
After receiving the user request, the server may generate a user-side feature corresponding to the user request, where the user-side feature may include a user attribute, an information preference, and other features.
The user attribute may be basic data of the user, such as age, gender, location, etc., and the information preference may be operation data of the user on the information, such as clicking, sharing, comment, playing duration, etc.
Step 102, generating a plurality of user side sub-vectors corresponding to the user side features by adopting a preset user side network, wherein each user side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight;
as an example, the target information may include any one or more of the following:
news, short video, music.
As an example, recall targets may include the following:
clicking, sharing, commenting and playing time.
In a specific implementation, a plurality of recall targets may be preset, and then a user-side network is trained according to the recall targets, where the user-side network may be a network model that adopts a neural network algorithm, such as DNN (Deep Neural Network ), and a recall target weight corresponding to each recall target may be set according to a current service period.
After the user-side features are obtained, the user-side features can be input into a trained user-side network, and the user-side network can adopt the user-side features to generate user-side sub-vectors corresponding to each recall target.
In an embodiment of the present application, the method may further include the following steps:
and acquiring a plurality of sample user side features, and training the plurality of sample user side features according to the plurality of recall targets to obtain a user side network.
In a specific implementation, as shown in fig. 2, the information recommendation may be composed of three parts, namely a feature extraction module, offline training, and an online service system, where the original features are obtained by collecting logs, the feature extraction module may obtain a plurality of sample user side features therefrom, and further may perform offline training on the obtained plurality of sample user side features according to a plurality of recall targets, to obtain a user side network.
After the user side network is obtained, the user side network can be released, and then the online information recommendation can be completed through the feature service, the recall service and the sequencing service in the online service system.
after obtaining the user side sub-vector, the recall target weight may be used to weight the plurality of user side sub-vectors, and then the weighted user side sub-vectors may be formed into the user side vector.
In an embodiment of the present application, step 103 may include the following sub-steps:
weighting the plurality of user side sub-vectors by adopting the recall target weight respectively; establishing connection to the weighted multiple user side sub-vectors to obtain a user side vector;
in practical application, recall target weights can be set by background staff, and for a plurality of user side sub-vectors corresponding to each recall target, recall target weights can be adopted for weighting, so that the weighted plurality of user side sub-vectors are connected, and a user side vector is obtained.
For example, there are 3 recall targets l1, l2, l3, where the recall targets weight is W1, W2, W3, and the user side subvectors generated by the user side network are UV1, UV2, UV3, respectively, so that the user side vector uv=u1×w1concatu2×w2concatu3×w3 can be determined.
Where "×" may characterize each dimension in the vector as multiplied by a weight, respectively, "concat" may characterize connecting the vectors.
And 104, searching target information matched with the user side vector from a preset information database, and recommending the target information to a user.
In a specific implementation, a plurality of pieces of information (items) can be stored in a preset information database, so that after a user side vector is obtained, target information matched with the user side vector can be searched for, then the target information can be pushed to a client, and a user can view the target information in the client.
In an embodiment of the present application, the method may further include the following steps:
for the information in the information database, acquiring information side characteristics corresponding to the information; generating a plurality of information side sub-vectors corresponding to the information side characteristics by adopting a preset information side network, wherein each information side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight; and adopting the recall target weight to form the information side sub-vectors into information side vectors.
As an example, the information-side network may be a network model employing a neural network algorithm, such as DNN (Deep Neural Network ).
For each piece of information stored in the information database, information side characteristics corresponding to the information can be obtained, then a preset information side network can be adopted to generate a plurality of information side sub-vectors corresponding to the information side characteristics, and further recall target weights are adopted to form the information side vectors from the plurality of information side sub-vectors.
In an embodiment of the present application, the step of forming the information-side sub-vectors into the information-side vector using the recall target weight may include the following sub-steps:
weighting the information side sub-vectors by adopting the recall target weight respectively; and establishing connection for the weighted information side sub-vectors to obtain the information side vector.
In practical application, for a plurality of information side sub-vectors corresponding to each recall target, the recall target weight may be used to perform weighting, and then the weighted information side sub-vectors are connected to obtain an information side vector.
In an embodiment of the present application, the method may further include the following steps:
and acquiring a plurality of sample information side features, and training the sample information side features according to the plurality of recall targets to obtain an information side network.
In a specific implementation, the original features can be obtained by collecting logs, the feature extraction module can obtain a plurality of sample information side features from the original features, and further offline training can be performed on the obtained plurality of sample information side features according to a plurality of recall targets to obtain an information side network.
In an embodiment of the present application, step 104 may include the following sub-steps:
for a plurality of pieces of information in a preset information database, similarity calculation is respectively carried out on the user side vector and the information side vector to obtain a plurality of recall target similarities; and sorting the information according to the recall target similarity, and selecting one or more information sorted in front as target information.
Because the user side vector and the information side vector are obtained, a manner of calculating Euclidean distance can be adopted to obtain recall target similarity between the user and a plurality of pieces of information, then the plurality of pieces of information can be ordered according to the recall target similarity, and one or more pieces of information ordered in front are selected as target information so as to recommend the target information.
In an embodiment of the present application, the step of calculating the similarity between the user side vector and the information side vector to obtain a plurality of recall target similarities may include the following sub-steps:
calculating Euclidean distance between the user side vector and the information side vector; and determining the recall target similarity by adopting the Euclidean distance.
Taking 3 recall targets as an example, as shown in fig. 3, in an offline stage, by carrying out feature normalization on input features user_input and item_input to obtain user_normal_input and item_normal_input, and then through full connection and relu activation, three hidden layers h1_user, h2_user and h3_user are obtained, so that a User side network and an Item side network which fit 3 recall targets user_vec_task can be trained.
Correspondingly, in the online stage, the matching target information can be determined by calculating the Euclidean distance between the user side vector and the information side vector, and then adopting the Euclidean distance and combining the Sigmoid function, so that the matching target information can be recommended to the user.
In an embodiment of the present application, the method may further include the following steps:
and carrying out normalization processing on the user side characteristics.
Because the input features are normalized in the training process of the user side network and the information side network, the user side features can be normalized after the user side features are obtained.
In an embodiment of the present application, the method may further include the following steps:
and adjusting recall target weights corresponding to the recall targets.
Because the recall targets are different in different service periods, that is, the condition of service turning exists, the recall target weight corresponding to the recall target can be adjusted, for example, long videos can be most favored by users when being previously considered to be played, and further benefits are created for the platform, the recall target weight for setting the recall target to be the play duration is larger, the video considered to be commented at present can be more favored by users, and the recall target weight for setting the recall target to be commented is larger.
Because the recall target weight is adjusted, the adjusted recall target weight can be adopted to form a plurality of user side sub-vectors into a user side vector, namely the user side vector is adjusted, and the adjusted recall target weight is adopted to form a plurality of information side sub-vectors into an information side vector, namely the information side vector is adjusted, so that information matched with the adjusted recall target can be obtained, and the retraining of the network model is avoided.
In the embodiment of the application, when a user request is received, the user side characteristics corresponding to the user request are acquired, a preset user side network is adopted, a plurality of user side sub-vectors corresponding to the user side characteristics are generated, each user side sub-vector corresponds to a recall target, each recall target corresponds to a recall target weight, then the recall target weights are adopted, the plurality of user side sub-vectors form a user side vector, target information matched with the user side vectors is searched from a preset information database, and the target information is recommended to a user, so that information recommendation combined with the plurality of recall targets is realized, the recall targets can be timely adjusted during service diversion, and resource waste is avoided.
It should be noted that, for simplicity of description, the method embodiments are shown as a series of acts, but it should be understood by those skilled in the art that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments and that the acts referred to are not necessarily required by the embodiments of the present application.
Referring to fig. 4, a schematic structural diagram of an information recommendation device provided in an embodiment of the present application is shown, which may specifically include the following modules:
a user side feature obtaining module 401, configured to obtain, when a user request is received, a user side feature corresponding to the user request;
a user side sub-vector generation module 402, configured to generate a plurality of user side sub-vectors corresponding to the user side features by using a preset user side network, where each user side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight;
a user side vector component module 403, configured to use the recall target weight to component the plurality of user side sub-vectors into a user side vector;
and the information recommending module 404 is configured to search target information matched with the user side vector from a preset information database, and recommend the target information to a user.
In an embodiment of the present application, further includes:
the information side characteristic acquisition module is used for acquiring information side characteristics corresponding to the information of the information database;
the information side sub-vector generation module is used for generating a plurality of information side sub-vectors corresponding to the information side characteristics by adopting a preset information side network, each information side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight;
and the information side vector composition module is used for composing the plurality of information side sub-vectors into an information side vector by adopting the recall target weight.
In an embodiment of the present application, the user side vector component module 403 includes:
the user side weighting sub-module is used for weighting the plurality of user side sub-vectors by adopting the recall target weight respectively;
a user side establishing connection sub-module is used for establishing connection for the weighted multiple user side sub-vectors to obtain a user side vector;
the information side quantity composition module includes:
the information side weighting sub-module is used for weighting the plurality of information side sub-vectors by adopting the recall target weight respectively;
and the information side establishing connection sub-module is used for establishing connection for the weighted multiple information side sub-vectors to obtain an information side vector.
In an embodiment of the present application, the information recommendation module 404 includes:
the recall target similarity obtaining submodule is used for carrying out similarity calculation on the user side vector and the information side vector respectively for a plurality of pieces of information in a preset information database to obtain a plurality of recall target similarities;
and the sorting and selecting sub-module is used for sorting the plurality of information according to the recall target similarity and selecting one or more information sorted in front as target information.
In an embodiment of the present application, the recall target similarity obtaining submodule includes:
the Euclidean distance calculating unit is used for calculating Euclidean distances between the user side vector and the information side vector;
and the recall target similarity determining unit is used for determining recall target similarity by adopting the Euclidean distance.
In an embodiment of the present application, further includes:
and the weight adjustment module is used for adjusting the recall target weight corresponding to the recall target.
In an embodiment of the present application, further includes:
the user side network training module is used for acquiring a plurality of sample user side characteristics and training the plurality of sample user side characteristics according to the plurality of recall targets to obtain a user side network;
and the information side network training module is used for acquiring a plurality of sample information side characteristics, and training the sample information side characteristics according to the plurality of recall targets to obtain an information side network.
In an embodiment of the present application, further includes:
and the normalization processing module is used for performing normalization processing on the user side characteristics.
In an embodiment of the present application, the user-side feature includes any one or more of the following:
user attribute features, information preference features.
In an embodiment of the present application, the target information includes any one or more of the following:
news, short video, music;
the recall target includes a plurality of:
clicking, sharing, commenting and playing time.
In an embodiment of the present application, the user side network and/or the information side network is a network model that adopts a neural network algorithm.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
An embodiment of the present application further provides an electronic device, which may include a processor, a memory, and a computer program stored on the memory and capable of running on the processor, where the computer program implements the steps of the method as recommended by the information above when executed by the processor.
An embodiment of the present application further provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method as recommended by the information above.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present embodiments have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the present application.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal 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 terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The method, the device, the electronic equipment and the storage medium for recommending information are described in detail, and specific examples are applied to the description of the principles and the implementation modes of the application, and the description of the examples is only used for helping to understand the method and the core idea of the application; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (14)
1. A method of information recommendation, the method comprising:
when a user request is received, acquiring a user side characteristic corresponding to the user request;
generating a plurality of user side sub-vectors corresponding to the user side characteristics by adopting a preset user side network, wherein each user side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight;
the recall target weight is adopted, and the plurality of user side sub-vectors are formed into a user side vector;
searching target information matched with the user side vector from a preset information database, and recommending the target information to a user;
wherein, still include:
and acquiring a plurality of sample user side features, and training the plurality of sample user side features according to a plurality of recall targets to obtain a user side network.
2. The method as recited in claim 1, further comprising:
for the information in the information database, acquiring information side characteristics corresponding to the information;
generating a plurality of information side sub-vectors corresponding to the information side characteristics by adopting a preset information side network, wherein each information side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight;
and adopting the recall target weight to form the information side sub-vectors into information side vectors.
3. The method of claim 2, wherein the step of grouping the plurality of user-side sub-vectors into a user-side vector using the recall target weights comprises:
weighting the plurality of user side sub-vectors by adopting the recall target weight respectively;
establishing connection to the weighted multiple user side sub-vectors to obtain a user side vector;
the step of forming the information side sub-vectors into information side vectors by adopting the recall target weight includes:
weighting the information side sub-vectors by adopting the recall target weight respectively;
and establishing connection for the weighted information side sub-vectors to obtain the information side vector.
4. A method according to claim 2 or 3, wherein the step of finding target information matching the user side vector from a pre-set information database comprises:
for a plurality of pieces of information in a preset information database, similarity calculation is respectively carried out on the user side vector and the information side vector to obtain a plurality of recall target similarities;
and sorting the information according to the recall target similarity, and selecting one or more information sorted in front as target information.
5. The method of claim 4, wherein the step of computing similarities for the user side vector and the information side vector, respectively, to obtain a plurality of recall target similarities comprises:
calculating Euclidean distance between the user side vector and the information side vector;
and determining the recall target similarity by adopting the Euclidean distance.
6. A method according to claim 1 or 2 or 3, further comprising:
and adjusting recall target weights corresponding to the recall targets.
7. The method as recited in claim 1, further comprising:
and acquiring a plurality of sample information side features, and training the sample information side features according to a plurality of recall targets to obtain an information side network.
8. The method as recited in claim 1, further comprising:
and carrying out normalization processing on the user side characteristics.
9. The method of claim 1, wherein the user-side features comprise any one or more of:
user attribute features, information preference features.
10. The method of claim 1, wherein the target information comprises any one or more of:
news, short video, music;
the recall target includes a plurality of:
clicking, sharing, commenting and playing time.
11. The method according to claim 2, wherein the user side network and/or the information side network is a network model employing a neural network algorithm.
12. An apparatus for information recommendation, the apparatus comprising:
the user side characteristic acquisition module is used for acquiring user side characteristics corresponding to a user request when the user request is received;
the user side sub-vector generation module is used for generating a plurality of user side sub-vectors corresponding to the user side characteristics by adopting a preset user side network, each user side sub-vector corresponds to a recall target, and each recall target corresponds to a recall target weight;
the user side vector composition module is used for composing the plurality of user side sub-vectors into a user side vector by adopting the recall target weight;
the information recommending module is used for searching target information matched with the user side vector from a preset information database and recommending the target information to a user;
wherein, still include:
and the user side network training module is used for acquiring a plurality of sample user side characteristics, and training the plurality of sample user side characteristics according to a plurality of recall targets to obtain a user side network.
13. An information recommendation device comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, which when executed by the processor, implements the steps of the information recommendation method according to any one of claims 1 to 11.
14. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the method of information recommendation according to any one of claims 1 to 11.
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| CN201911423248.8A CN111209477B (en) | 2019-12-31 | 2019-12-31 | Information recommendation method and device, electronic equipment and storage medium |
| PCT/CN2020/129863 WO2021135701A1 (en) | 2019-12-31 | 2020-11-18 | Information recommendation method and apparatus, electronic device, and storage medium |
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| CN111209477B (en) * | 2019-12-31 | 2023-06-09 | 广州市百果园信息技术有限公司 | Information recommendation method and device, electronic equipment and storage medium |
| CN112949703A (en) * | 2021-02-23 | 2021-06-11 | 广州市百果园信息技术有限公司 | Video recall method and device and electronic equipment |
| CN113379449B (en) * | 2021-05-31 | 2022-10-04 | 北京达佳互联信息技术有限公司 | Multimedia resource recall method and device, electronic equipment and storage medium |
| CN113468347A (en) * | 2021-07-26 | 2021-10-01 | 北京明略软件***有限公司 | Method and device for data recall, electronic equipment and readable storage medium |
| CN113569148A (en) * | 2021-07-29 | 2021-10-29 | 北京快乐茄信息技术有限公司 | Target information recommendation method and device, electronic equipment and storage medium |
| CN114677168A (en) * | 2022-03-11 | 2022-06-28 | 百果园技术(新加坡)有限公司 | Resource recommendation method, device, equipment and medium |
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| US10657575B2 (en) * | 2017-01-31 | 2020-05-19 | Walmart Apollo, Llc | Providing recommendations based on user-generated post-purchase content and navigation patterns |
| CN107862022B (en) * | 2017-10-31 | 2020-12-11 | 中国科学院自动化研究所 | Culture resource recommendation system |
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| CN110399563A (en) * | 2019-08-05 | 2019-11-01 | 中国人民解放军军事科学院评估论证研究中心 | A kind of preference pattern and customized information active recommended method based on user behavior analysis |
| CN111209477B (en) * | 2019-12-31 | 2023-06-09 | 广州市百果园信息技术有限公司 | Information recommendation method and device, electronic equipment and storage medium |
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| CN110427463A (en) * | 2019-08-08 | 2019-11-08 | 腾讯科技（深圳）有限公司 | Search statement response method, device and server and storage medium |
| CN110543598A (en) * | 2019-09-06 | 2019-12-06 | 腾讯科技（深圳）有限公司 | information recommendation method and device and terminal |
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