Content recommendation method and device, equipment, medium and product

Abstract

The invention provides a content recommendation method and device, equipment, a medium and a product, and is applied to the technical field of artificial intelligence and data analysis. The method comprises the following steps: acquiring static data and dynamic data of a user account; wherein the static data is historical data and / or reserved data, and the dynamic data is data generated in real time based on current operation of a user; generating a plurality of feature vectors based on the static data and the dynamic data; wherein the information dimensions represented by different feature vectors are different; searching similar accounts according to the feature vectors; and recommending the target content to the user based on the similar accounts and the feature vectors.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence and data analysis technology, and more specifically, to a content recommendation method, device, equipment, medium and product. Background Art

[0002] With the development of Internet technology, more and more application platforms have launched corresponding content recommendation matching rules to enrich the user experience of different users.

[0003] However, in the prior art, when making matching recommendations to users, it usually only relies on simple tags or single multi-dimensional data analysis, and does not take into account the user's immediate needs, which can easily lead to inaccurate content matching recommendation results. Summary of the invention

[0004] In view of this, the present disclosure provides a content recommendation method, apparatus, device, medium and product for improving the accuracy of content recommendation.

[0005] One aspect of the present disclosure provides a content recommendation method, including: obtaining static data and dynamic data of a user account; wherein the static data is historical data and / or reserved data, and the dynamic data is data generated in real time based on the user's current operation; generating multiple feature vectors based on the static data and the dynamic data respectively; wherein different feature vectors represent different information dimensions; searching for similar accounts based on the feature vectors; and recommending target content to the user based on the similar accounts and the feature vectors.

[0006] According to an embodiment of the present disclosure, static data includes user data, associated data, and historical data. Obtaining static data and dynamic data of a user account includes: obtaining reserved information of the user account to obtain user data; based on a first interface, obtaining associated data of the user account on other platforms; wherein the first interface is an integrated interface for multiple platforms; and obtaining historical data based on historical usage of the user account.

[0007] According to an embodiment of the present disclosure, obtaining static data and dynamic data of a user account includes: sending a real-time acquisition request to a second interface of the target platform; wherein the real-time acquisition request is used to obtain operation data of the user account on the target platform; parsing the received response information to obtain the dynamic data of the user account; wherein the response information is generated by the target platform based on the real-time acquisition request.

[0008] According to an embodiment of the present disclosure, multiple feature vectors are generated based on static data and dynamic data respectively, including: extracting multiple target features from the static data and dynamic data respectively; wherein different types of data are extracted in different ways; and generating multiple feature vectors based on the multiple target features.

[0009] According to an embodiment of the present disclosure, searching for similar accounts based on feature vectors includes: constructing at least one feature group based on the feature vectors, wherein the feature group includes at least two feature vectors; querying a first account based on the feature group; respectively calculating the similarity between the first account and the user account; and determining similar accounts based on the similarity.

[0010] According to an embodiment of the present disclosure, target content is recommended to users based on similar accounts and feature vectors, including: generating a recommendation list based on feature vectors and similar accounts; predicting the user's interest level in each recommended content in the recommendation list based on a collaborative filtering algorithm; sorting the recommended content based on the interest level and displaying the recommended content to the user.

[0011] Another aspect of the present disclosure provides a content recommendation device, including: an acquisition module, used to acquire static data and dynamic data of a user account; wherein the static data is historical data and / or reserved data, and the dynamic data is data generated in real time based on the user's current operation; a generation module, used to generate multiple feature vectors based on the static data and the dynamic data respectively; wherein different feature vectors represent different information dimensions; a search module, used to search for similar accounts based on the feature vectors; and a recommendation module, used to recommend target content to users based on similar accounts and feature vectors.

[0012] Another aspect of the present disclosure provides an electronic device, including: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0013] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0014] Another aspect of the present disclosure provides a computer program product, comprising computer executable instructions, which are used to implement the method described above when the instructions are executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0016] Figure 1 The application scenario diagram of the content recommendation method, device, equipment, medium and product according to the embodiment of the present disclosure is schematically shown. ;

[0017] Figure 2 A flowchart of a content recommendation method according to an embodiment of the present disclosure is schematically shown;

[0018] Figure 3A flowchart of obtaining static data and dynamic data of a user account according to an embodiment of the present disclosure is schematically shown;

[0019] Figure 4 A flowchart of obtaining static data and dynamic data of a user account according to another embodiment of the present disclosure is schematically shown;

[0020] Figure 5 Schematically shows a flow chart of generating multiple feature vectors based on static data and dynamic data respectively according to another embodiment of the present disclosure;

[0021] Figure 6 A flowchart of searching for similar accounts based on feature vectors according to another embodiment of the present disclosure is schematically shown;

[0022] Figure 7 Schematically shows a flow chart of recommending target content to a user based on similar accounts and feature vectors according to another embodiment of the present disclosure;

[0023] Figure 8 A structural block diagram of a content recommendation device according to an embodiment of the present disclosure is schematically shown; and

[0024] Fig. 9 A block diagram of an electronic device suitable for implementing a content recommendation method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0025] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0026] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0027] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0028] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0029] It should be noted that in the technical solution of the present invention, the collection, use, storage, sharing and transfer of data (for example, including but not limited to user personal information) involved are in compliance with the provisions of relevant laws and regulations, and it is necessary to inform the user and obtain the user's consent or authorization. When applicable, the user's personal information is de-identified and / or anonymized and / or encrypted.

[0030] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security, and national security.

[0031] In the embodiments of the present disclosure, before obtaining or collecting user personal information, the user has been informed and the user's consent or authorization has been obtained.

[0032] An embodiment of the present disclosure provides a content recommendation method, which includes: obtaining static data and dynamic data of a user account; wherein the static data is historical data and / or reserved data, and the dynamic data is data generated in real time based on the user's current operation; generating multiple feature vectors based on the static data and the dynamic data respectively; wherein different feature vectors represent different information dimensions; searching for similar accounts based on the feature vectors; and recommending target content to the user based on the similar accounts and the feature vectors.

[0033] Figure 1 The following schematically shows an application scenario diagram of the content recommendation method, apparatus, device, medium and product according to an embodiment of the present disclosure. It should be noted that: Figure 1 What is shown are merely examples of application scenarios to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0034] like Figure 1As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0035] The user may use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only for example).

[0036] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0037] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0038] It should be noted that the content recommendation method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the content recommendation device provided in the embodiment of the present disclosure can generally be set in the server 105. The content recommendation method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the content recommendation device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Alternatively, the content recommendation method provided in the embodiment of the present disclosure can also be executed by the first terminal device 101, the second terminal device 102 or the third terminal device 103, or by other terminal devices that are different from the first terminal device 101, the second terminal device 102 or the third terminal device 103. Accordingly, the content recommendation device provided in the embodiment of the present disclosure may also be arranged in the first terminal device 101, the second terminal device 102 or the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0039] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0040] The following will be based on Figure 1 The scene described by Figure 2~Figure 7 The content recommendation method of the disclosed embodiment is described in detail.

[0041] Figure 2 The flowchart of the content recommendation method according to the embodiment of the present disclosure is schematically shown.

[0042] like Figure 2 As shown, the content recommendation method of this embodiment includes operations S210 to S240.

[0043] In operation S210, static data and dynamic data of the user account are obtained; wherein the static data is historical data, and the dynamic data is data generated in real time based on the user's current operation.

[0044] In some embodiments, static data and dynamic data of a user account are obtained separately, wherein static data refers to data that remains unchanged or changes very slowly over a period of time, and dynamic data refers to data that is updated regularly over time.

[0045] In the specific implementation process, static data refers to historical data or reserved data, which may include basic information, professional background, hobbies, social network relationships, etc. filled in by users themselves, or historical usage records of users. Taking the conference platform as an example, historical data in static data may include historical conference attendance data and historical interaction records of the user account. Dynamic data is data generated based on real-time operations of users on the platform, including real-time interaction data of users at the current moment, such as immediate needs, interactive comments, search content, etc.

[0046] In operation S220, a plurality of feature vectors are generated based on the static data and the dynamic data respectively; wherein different feature vectors represent different information dimensions.

[0047] In some embodiments, corresponding features are extracted from static data and dynamic data respectively, and feature vectors are constructed based on the extracted features. Among them, the feature extraction method of static data is different from the feature extraction method of dynamic data. After the corresponding features are extracted, the corresponding feature vectors are constructed based on the extracted features, wherein the types of feature vectors may include static feature vectors and dynamic feature vectors, and each type may also include multiple sub-vectors. Static feature vectors mainly characterize the inherent attributes of users or objects, and the information dimension is relatively stable. Dynamic feature vectors are mainly used to characterize the behavior patterns, change trends, etc. of users or objects, and the information dimension is enriched over time. Dynamic feature vectors at different time points can reflect different information dimensions, such as short-term behavior patterns, long-term trends, etc.

[0048] In operation S230, similar accounts are searched based on the feature vectors.

[0049] In some embodiments, accounts similar to the current user account are determined based on feature vectors of multiple different dimensions, wherein the similar accounts can be used as target content recommended to the user or as a basis for acquiring the target content.

[0050] In operation S240 , target content is recommended to the user based on the similar accounts and the feature vectors.

[0051] In some embodiments, the recommended content is different in different recommendation scenarios. For example, if the current platform is a social platform, the recommended content may be other accounts that the user is interested in. If the current platform is a shopping platform, the recommended content may be other products that the user is interested in.

[0052] The content recommendation method provided by the present disclosure generates feature vectors of multiple information dimensions by acquiring static data and dynamic data of user accounts, which helps to more comprehensively describe user needs, thereby effectively improving the accuracy and comprehensiveness of content recommendations. In addition, the solution combining dynamic dimensions and static dimensions is used for content recommendation, which effectively captures different aspects of the data, can understand the user's real-time behavior patterns and change trends, and effectively improves the interpretability and flexibility of content recommendations.

[0053] Figure 3 The flowchart of obtaining static data and dynamic data of a user account according to an embodiment of the present disclosure is schematically shown.

[0054] like Figure 3 As shown, the acquisition of static data and dynamic data of a user account in this embodiment includes operations S310 to S330.

[0055] In operation S310, the reservation information of the user account is acquired to obtain the user data.

[0056] In operation S320, based on the first interface, the associated data of the user account on other platforms is obtained; wherein the first interface is an integrated interface of multiple platforms.

[0057] In operation S330, historical data is obtained according to the historical usage of the user account.

[0058] In some embodiments, static data may include, for example, user data, association data, historical data, etc.

[0059] In the specific implementation process, the user data is filled in by the user when registering the account, for example, it may include name, occupation, company, position, gender, interest tags, etc. The historical data is the historical usage of the account. Taking the conference platform as an example, the historical data of the user account may be, for example, the user's past attendance records and interaction records in each conference. The associated data may be the social data disclosed by the user on other platforms, for example, it may include the user's professional background, hobbies, social relationships, etc. on other platforms, where the first interface is the Application Programming Interface (API) interface of the integrated social media platform. The API interface of the integrated social platform refers to an interface that allows data exchange and function integration between different social media platforms or between social media platforms and third-party applications. Data acquisition requests can be sent to other platforms through the first interface to obtain the user's associated data on other platforms.

[0060] The content recommendation method provided by the embodiment of the present disclosure obtains the user's static data in a variety of ways to improve the comprehensiveness of user data acquisition, thereby effectively improving the accuracy of user preference analysis and content recommendation prediction.

[0061] Figure 4 A flowchart for obtaining static data and dynamic data of a user account according to another embodiment of the present disclosure is schematically shown.

[0062] like Figure 4 As shown, the acquisition of static data and dynamic data of a user account in this embodiment includes operations S410 to S420.

[0063] In operation S410, a real-time acquisition request is sent to a second interface of the target platform; wherein the real-time acquisition request is used to obtain operation data of the user account on the target platform.

[0064] In operation S420, the received response information is parsed to obtain dynamic data of the user account; wherein the response information is generated by the target platform based on the real-time acquisition request.

[0065] In some embodiments, a real-time acquisition request is sent to the second interface of the target platform to obtain the operation data of the user account on the target platform. The target platform is the platform where the user account is currently located, and the second interface is a real-time data interface, which can obtain the latest interactive information of the user on the platform in real time by programming, such as likes, comments, shares, etc. The second interface can be the first interface or other interfaces except the first interface.

[0066] In the specific implementation process, the dynamic data can be obtained by sending a real-time acquisition request to the second interface. When the request is successful, the target platform will generate and return a response message based on the received real-time acquisition request, wherein the response message includes the real-time data of the requested user account, and the dynamic data of the user account is obtained by parsing the response message received and returned by the target platform.

[0067] The content recommendation method provided by the embodiment of the present disclosure, on the basis of static data, further obtains the user's dynamic data, so as to accurately analyze the user's interests and preferences, improve the accuracy and flexibility of content recommendation, and ensure that the recommended content is always synchronized with the user's needs, so that the user can obtain timely feedback and improve user satisfaction.

[0068] Figure 5 A flowchart of generating multiple feature vectors based on static data and dynamic data respectively according to another embodiment of the present disclosure is schematically shown.

[0069] like Figure 5As shown, the method of generating multiple feature vectors based on static data and dynamic data respectively in this embodiment includes operations S510 to S520.

[0070] In operation S510, a plurality of target features are extracted from the static data and the dynamic data respectively; wherein different types of data are extracted in different ways.

[0071] In operation S520, a plurality of feature vectors are generated based on the plurality of target features.

[0072] In some embodiments, the feature extraction method for static data is different from the target feature extraction method for dynamic data.

[0073] Static data feature extraction can include manual coding and feature selection, etc. Manual coding means that data such as gender and age can be directly converted into numerical features through coding; feature selection means that statistical methods (such as correlation coefficients) or machine learning algorithms (such as recursive feature elimination) can be used to select the most useful features for target prediction.

[0074] Dynamic data feature extraction can include, for example, time series analysis, sliding windows, event monitoring, etc. Among them, time series analysis refers to trend analysis and seasonal decomposition of time series data to extract features such as periodicity and trend; sliding windows refer to sliding a window of fixed length on dynamic data and calculating statistics (such as mean and standard deviation) within the window as features. Event detection refers to identifying key events in dynamic data (such as purchase behavior and click behavior) and generating features based on these events.

[0075] After the corresponding features are extracted, the corresponding feature vectors are constructed based on the extracted features, wherein the types of feature vectors may include, for example, static feature vectors and dynamic feature vectors, and each type may also include multiple sub-vectors.

[0076] Static feature vectors mainly represent the inherent attributes of users or objects, and the information dimension is relatively stable. For example, they can include the user's occupation feature vector, interest feature vector, and social network relationship vector. Dynamic feature vectors are mainly used to represent the behavior patterns and change trends of users or objects. For example, they can include interaction data vectors. Compared with static feature vectors, the information dimension changes with time. Dynamic feature vectors at different time points can reflect different information dimensions, such as short-term behavior patterns and long-term trends.

[0077] Figure 6 The flowchart of searching for similar accounts based on feature vectors according to another embodiment of the present disclosure is schematically shown.

[0078] like Figure 6As shown, searching for similar accounts according to feature vectors in this embodiment includes operations S610 to S640.

[0079] In operation S610, at least one feature group is constructed based on the feature vectors, wherein the feature group includes at least two feature vectors.

[0080] In operation S620, the first account is queried according to the feature group.

[0081] In operation S630, similarities between the first account and the user account are calculated respectively.

[0082] In operation S640, similar accounts are determined based on the similarity.

[0083] In some embodiments, the most valuable features for a specific task can be selected from all available feature vectors for different scenarios, and the features of the snow top can be combined into a feature group including at least two feature vectors. The feature group can be flexibly designed according to actual needs. For example, different feature groups can be constructed based on user behavior patterns, interest preferences, etc.

[0084] Based on at least one feature group, a first account is queried on the target platform, wherein the first account is an account that has a certain similarity with the user account. After the first account is queried, the similarity between each first account and the user account is calculated respectively. Among them, the similarity calculation method may include, for example, cosine similarity, Euclidean distance, Pearson correlation coefficient, etc., which can be specifically selected according to the type of feature vector and task requirements, and the embodiment of the present disclosure is not limited here.

[0085] After obtaining the similarity calculation results between each first account and the user account, the first accounts are sorted based on the similarity scores, and similar accounts are screened out from the first accounts based on a preset threshold, wherein the similar accounts are accounts with similarity scores higher than the preset threshold.

[0086] The disclosed embodiments construct different feature groups and select different features to adapt to different task requirements and data characteristics, and improve the comprehensiveness of account feature characterization, thereby improving the accuracy and reliability of similarity calculation.

[0087] Figure 7 The flowchart of recommending target content to users based on similar accounts and feature vectors according to another embodiment of the present disclosure is schematically shown.

[0088] like Figure 7 As shown, the method of recommending target content to a user based on similar accounts and feature vectors in this embodiment includes operations S710 to S730.

[0089] In operation S710 , a recommendation list is generated according to the feature vector and similar accounts.

[0090] In operation S720 , the user's interest level in each recommended content in the recommendation list is predicted based on a collaborative filtering algorithm.

[0091] In operation S730, the recommended contents are sorted based on the interest level and displayed to the user.

[0092] In some embodiments, static features such as the interests, hobbies, and professional background of the participants can be used to generate a recommendation list related to them. The user's interest in each recommended content in the recommendation list is predicted based on a collaborative filtering algorithm, and the recommended content is sorted based on the interest level to generate a personalized recommendation list for the user, and the user is presented with the recommended content. Among them, different features have different weights on the recommended content, and the weight can be adjusted according to actual needs, which is not limited here. Among them, the recommended content can be other accounts that the user is interested in, or items, videos, etc. that the user is interested in.

[0093] Based on the above content recommendation method, the present disclosure also provides a content recommendation device. Figure 8 The device is described in detail.

[0094] Figure 8 The structure block diagram of the content recommendation device according to an embodiment of the present disclosure is schematically shown.

[0095] like Figure 8 As shown, the content recommendation device 800 of this embodiment includes an acquisition module 810 , a generation module 820 , a search module 830 and a recommendation module 840 .

[0096] The acquisition module 810 is used to acquire static data and dynamic data of the user account; wherein the static data is historical data and / or reserved data, and the dynamic data is data generated in real time based on the user's current operation. In one embodiment, the acquisition module 810 can be used to perform the operation S210 described above, which will not be repeated here.

[0097] The generating module 820 is used to generate multiple feature vectors based on the static data and the state data respectively; wherein different feature vectors represent different information dimensions. In one embodiment, the generating module 820 can be used to perform the operation S220 described above, which will not be described in detail here.

[0098] The search module 830 is used to search for similar accounts according to the feature vector. In one embodiment, the search module 830 can be used to perform the operation S230 described above, which will not be described in detail here.

[0099] The recommendation module 840 is used to recommend target content to the user based on similar accounts and feature vectors. In one embodiment, the recommendation module 840 can be used to perform the operation S240 described above, which will not be described in detail here.

[0100] According to an embodiment of the present disclosure, any multiple modules among the acquisition module 810, the generation module 820, the search module 830 and the recommendation module 840 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 810, the generation module 820, the search module 830 and the recommendation module 840 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the acquisition module 810, the generation module 820, the search module 830 and the recommendation module 840 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0101] Fig. 9 A block diagram of an electronic device suitable for implementing a content recommendation method according to an embodiment of the present disclosure is schematically shown.

[0102] like Fig. 9 As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 to a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include an onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0103] In RAM 903, various programs and data required for the operation of electronic device 900 are stored. Processor 901, ROM 802 and RAM 903 are connected to each other via bus 904. Processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 902 and / or RAM 903. It should be noted that the program can also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0104] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the input / output (I / O) interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage portion 908 as needed.

[0105] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0106] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above and / or one or more memories other than ROM 902 and RAM 903.

[0107] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the content recommendation method provided by the embodiment of the present disclosure.

[0108] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 901. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0109] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0110] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0111] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0112] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0113] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0114] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A content recommendation method, characterized in that: The method comprises: Obtain static data and dynamic data of the user account; wherein the static data is historical data and / or reserved data, and the dynamic data is data generated in real time based on the user's current operation; Generate multiple feature vectors based on the static data and the dynamic data respectively; wherein different feature vectors represent different information dimensions; Searching for similar accounts based on the feature vector; Recommend target content to users based on similar accounts and feature vectors.

2. The content recommendation method according to claim 1, characterized in that: The static data includes user data, associated data, and historical data. The static data and dynamic data of the user account are obtained, including: Get the reserved information of the user account and obtain the user data; Based on the first interface, obtaining the associated data of the user account on other platforms; wherein the first interface is an integrated interface of multiple platforms; Historical data is obtained according to historical usage of the user account.

3. The content recommendation method according to claim 1, characterized in that: The acquisition of static data and dynamic data of the user account includes: Sending a real-time acquisition request to the second interface of the target platform; wherein the real-time acquisition request is used to obtain the operation data of the user account on the target platform; Parse the received response information to obtain dynamic data of the user account; wherein the response information is generated by the target platform based on the real-time acquisition request.

4. The content recommendation method according to claim 1, characterized in that: The generating a plurality of feature vectors based on the static data and the dynamic data respectively comprises: Extracting a plurality of target features from the static data and the dynamic data respectively; wherein different types of data are extracted in different ways; A plurality of feature vectors are generated based on the plurality of target features.

5. The content recommendation method according to claim 1, characterized in that: The searching for similar accounts according to the feature vector comprises: constructing at least one feature group based on the feature vectors, wherein the feature group includes at least two feature vectors; querying a first account according to the feature group; Calculating the similarity between the first account and the user account respectively; Similar accounts are determined based on the similarity.

6. The content recommendation method according to claim 1, characterized in that: The recommending target content to the user based on similar accounts and feature vectors includes: generating a recommendation list based on the feature vector and similar accounts; Predict the user's interest in each recommended content in the recommendation list based on collaborative filtering algorithm; The recommended contents are sorted based on the interest level and displayed to the user.

7. A content recommendation device, characterized in that: The device comprises: An acquisition module, used to acquire static data and dynamic data of a user account; wherein the static data is historical data and / or reserved data, and the dynamic data is data generated in real time based on the user's current operation; A generating module, used to generate a plurality of feature vectors based on the static data and the dynamic data respectively; wherein different feature vectors represent different information dimensions; A search module, used to search for similar accounts according to the feature vector; The recommendation module is used to recommend target content to users based on similar accounts and feature vectors.

8. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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