A content recommendation method

Abstract

The present application relates to a content recommendation method, device, electronic device and computer-readable medium. The method includes: obtaining user information based on a user's recommendation request; obtaining multiple preliminary selected contents based on the user information; generating user features, request features and multiple content features based on the user information, the recommendation request and the multiple preliminary selected contents respectively; inputting the user features, the request features and the multiple content features into an interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents, wherein the interest recommendation model is generated by joint training of a deep recommendation model and a multi-task learning model; and determining at least one content to be recommended from the multiple preliminary selected contents based on the multiple interest scores. The content recommendation method and device involved in the present application can analyze the user's interest from multiple angles, and comprehensively generate recommended content based on various information, thereby improving recommendation efficiency and user satisfaction.

Description

Technical Field

[0001] The present application relates to the field of computer information processing, and in particular, to a content recommendation method. Background Art

[0002] At present, with the rapid development of computers, the peak daily upload volume of various multimedia data (such as graphic data and video data) from various source channels has exceeded millions or even tens of millions. More and more people are beginning to enjoy browsing short videos and watching exciting video content quickly and efficiently.

[0003] The recommendation system is an interactive product function. The product provides a carrier for the recommendation system. Users use the product to reach and trigger the recommendation system, and the recommendation system provides users with personalized recommendations, thereby improving the user experience. Unlike search engines, the recommendation system is essentially a technical means to help users find information of interest from a large amount of information when user needs are unclear. The recommendation system combines user information and content information, uses machine learning technology to build a user interest model, and provides users with accurate personalized recommendations.

[0004] With the development of deep learning, deep learning has also been greatly used in the field of recommendation systems. A series of recommendation algorithms based on deep learning have gradually come out. In addition to being used in shopping software, recommendation systems are also widely used in consulting recommendations of news apps and video recommendations of video software.

[0005] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0006] The present application provides a content recommendation method that can analyze the user's interest from multiple angles and comprehensively generate recommended content based on various information, thereby improving recommendation efficiency and user satisfaction.

[0007] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0008] According to one aspect of the present application, a content recommendation method is proposed, which includes: obtaining user information based on a user's recommendation request; obtaining multiple preliminary selected contents based on the user information; generating user features, request features, and multiple content features based on the user information, the recommendation request, and the multiple preliminary selected contents, respectively; inputting the user features, the request features, and the multiple content features into an interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents, the interest recommendation model being generated by joint training of a deep recommendation model and a multi-task learning model; and determining at least one content to be recommended from the multiple preliminary selected contents based on the multiple interest scores.

[0009] In an exemplary embodiment of the present application, it also includes: generating an initial model through a deep recommendation model and a multi-task learning model; training the initial model based on multiple user recommendation requests and multiple content recommendation results to generate the interest recommendation model.

[0010] In an exemplary embodiment of the present application, obtaining multiple preliminary selected contents based on the user information includes: extracting a pre-stored user portrait and a user vector based on a user identifier in the user information; extracting other users based on the user portrait to generate a list of other users; and generating the multiple preliminary selected contents based on the user portrait, the user vector, and the list of other users.

[0011] In an exemplary embodiment of the present application, generating the plurality of preliminary selected contents based on the user portrait, the user vector, and the other user list further includes: performing exposure and deduplication on the plurality of preliminary selected contents based on the user information.

[0012] In an exemplary embodiment of the present application, user characteristics, request characteristics, and multiple content characteristics are generated based on the user information, the recommendation request, and the multiple preliminary selected contents, respectively, including: organizing the user information, the recommendation request, and the multiple preliminary selected contents according to preset strategies to generate the user characteristics, the request characteristics, and the multiple content characteristics.

[0013] In an exemplary embodiment of the present application, the user features, the request features, and the multiple content features are input into an interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents, including: inputting the user features, the request features, and the multiple content features into the interest recommendation model; the deep recommendation model part of the interest recommendation model generates a combined feature based on the user features, the request features, and the multiple content features; the multi-task learning model part of the interest recommendation model performs calculations based on the combined feature to generate multiple interest scores corresponding to the multiple content features; wherein the interest score corresponding to the content feature represents the user's interest level in the preliminary selected contents.

[0014] In an exemplary embodiment of the present application, the multi-task learning model part in the interest recommendation model performs calculations based on the combined features to generate interest scores corresponding to the multiple content features, including: the multi-task learning model part in the interest recommendation model performs calculations based on the combined features to generate click probability and propagation probability; and generates interest scores corresponding to the multiple content features based on the click probability, the propagation probability and their corresponding weights.

[0015] In an exemplary embodiment of the present application, determining at least one content to be recommended from the multiple preliminary selected contents based on the multiple interest scores includes: sorting the multiple preliminary selected contents based on the multiple interest scores; and extracting at least one preliminary selected content as the content to be recommended according to the sorting.

[0016] In an exemplary embodiment of the present application, an initial model is generated by a deep recommendation model and a multi-task learning model, including: setting the DNN hidden layer in the deep recommendation model to three layers, each layer with a dropout factor; using the output data of the deep recommendation model as the input data of the multi-task learning model; setting the expert network of the multi-task learning model to two, and the expert units to sixteen; setting the DNN hidden layer in the expert network of the multi-task learning model to two layers; and generating the initial model based on the above settings.

[0017] In an exemplary embodiment of the present application, the initial model is trained based on multiple user recommendation requests and multiple content recommendation results to generate the interest recommendation model, including: generating a training sample set based on multiple user recommendation requests; setting sample labels for samples in the training sample set based on the content recommendation results; training the initial model based on the training sample set data with sample labels; and generating the interest recommendation model when the training parameters meet a threshold.

[0018] According to one aspect of the present application, a content recommendation device is proposed, which includes: an information module, which is used to obtain user information based on a user's recommendation request; a preliminary selection module, which is used to obtain multiple preliminary selected contents based on the user information; a feature module, which is used to generate user features, request features, and multiple content features based on the user information, the recommendation request, and the multiple preliminary selected contents respectively; a scoring module, which is used to input the user features, the request features, and the multiple content features into an interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents, and the interest recommendation model is generated by joint training of a deep recommendation model and a multi-task learning model; a recommendation module, which is used to determine at least one content to be recommended from the multiple preliminary selected contents based on the multiple interest scores.

[0019] In an exemplary embodiment of the present application, it also includes: a training module, used to generate an initial model through a deep recommendation model and a multi-task learning model; training the initial model based on multiple user recommendation requests and multiple content recommendation results to generate the interest recommendation model.

[0020] According to one aspect of the present application, an electronic device is proposed, which includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0021] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processor, the method described above is implemented.

[0022] According to the content recommendation method, device, electronic device and computer-readable medium of the present application, user information is obtained based on a recommendation request of the user; multiple preliminary selected contents are obtained based on the user information; user features, request features and multiple content features are respectively generated based on the user information, the recommendation request and the multiple preliminary selected contents; the user features, the request features and the multiple content features are input into an interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents, and the interest recommendation model is generated by joint training of a deep recommendation model and a multi-task learning model; at least one content to be recommended is determined from the multiple preliminary selected contents based on the multiple interest scores, so that the user's interest can be analyzed from multiple angles, and recommended content can be comprehensively generated by combining various information, thereby improving recommendation efficiency and user satisfaction.

[0023] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and other objects, features and advantages of the present application will become more apparent by describing in detail the exemplary embodiments thereof with reference to the accompanying drawings. The accompanying drawings described below are only some embodiments of the present application, and it is clear to a person skilled in the art that other accompanying drawings can be obtained from these accompanying drawings without creative effort.

[0025] Figure 1 The present invention is a system block diagram showing a content recommendation method and device according to an exemplary embodiment.

[0026] Figure 2 The present invention is a flowchart of a content recommendation method according to an exemplary embodiment.

[0027] Figure 3It is a schematic diagram of a content recommendation method according to an exemplary embodiment.

[0028] Figure 4 The present invention is a flowchart of a content recommendation method according to another exemplary embodiment.

[0029] Figure 5 is a schematic diagram of a content recommendation method according to another exemplary embodiment. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0031] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0034] It should be understood that although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be referred to as the second component without departing from the teachings of the concepts of the present application. As used herein, the term "and / or" includes any one of the associated listed items and all combinations of one or more.

[0035] Those skilled in the art will appreciate that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present application, and therefore cannot be used to limit the scope of protection of the present application.

[0036] Figure 1 The present invention is a system block diagram showing a content recommendation method and device according to an exemplary embodiment.

[0037] like Figure 1 As shown, the system architecture 10 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0038] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as news applications, live video applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0039] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0040] The server 105 may be a server that provides various services, such as a background management server that provides support for news websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the recommendation request from the user, and feed back the processing result (such as at least one content to be recommended) to the terminal device.

[0041] The server 105 may, for example, generate an initial model through a deep recommendation model and a multi-task learning model; the server 105 may, for example, train the initial model based on multiple user recommendation requests and multiple content recommendation results to generate the interest recommendation model.

[0042] The server 105 may, for example, send the interest recommendation model to the terminal devices 101 , 102 , 103 so as to perform model calculations on the terminal devices 101 , 102 , 103 . The server 105 may also, for example, perform model calculations on the service side, but the present application is not limited thereto.

[0043] The server 105 may, for example, obtain a user's recommendation request from the terminal devices 101, 102, 103, and obtain user information based on the recommendation request; the server 105 may, for example, obtain multiple preliminary selected contents based on the user information; the server 105 or the terminal devices 101, 102, 103 may, for example, respectively generate user features, request features, and multiple content features based on the user information, the recommendation request, and the multiple preliminary selected contents; the server 105 or the terminal devices 101, 102, 103 may, for example, input the user features, the request features, and the multiple content features into an interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents, and the interest recommendation model is generated by joint training of a deep recommendation model and a multi-task learning model; the server 105 or the terminal devices 101, 102, 103 may, for example, determine at least one content to be recommended from the multiple preliminary selected contents based on the multiple interest scores.

[0044] Server 105 can be a physical server, or can be composed of multiple servers, for example. A part of server 105 can be used as the service system in this application, for receiving user recommendation requests; and a part of server 105 can also be used as the recommendation system in this application, for generating multiple recommended contents based on user information.

[0045] It should be noted that the content recommendation method provided in the embodiment of the present application can be executed by the server 105 and / or the terminal devices 101, 102, 103, and accordingly, the content recommendation device can be set in the server 105 and / or the terminal devices 101, 102, 103. The web page provided for users to browse news is generally located in the terminal devices 101, 102, 103.

[0046] Figure 2 2 is a flowchart of a content recommendation method according to an exemplary embodiment. The content recommendation method 20 at least includes steps S202 to S210.

[0047] like Figure 2 As shown, in S202, user information is obtained based on the user's recommendation request. When the user pulls down to refresh the client homepage of the preset application, a recommendation request can be generated. The user information is obtained based on the client request, and the user information can be the user's ID, registration time, age, gender, and other information. The recommendation request can also include the user's current browsing page, login time, context features, device model, system, request time, request IP, location, and other information.

[0048] In S204, a plurality of preliminary selected contents are obtained based on the user information. A pre-stored user portrait and a user vector are extracted based on the user identifier in the user information; other users are extracted based on the user portrait to generate a list of other users; and the plurality of preliminary selected contents are generated based on the user portrait, the user vector, and the list of other users.

[0049] In one embodiment, the method further includes: performing exposure deduplication on the plurality of preliminarily selected contents based on the user information, that is, the contents that have been shown to the user cannot be pushed to the user again, and the exposure deduplication filters out the contents that have been shown to the user.

[0050] In S206, based on the user information, the recommendation request, and the multiple preliminary selected contents, user features, request features, and multiple content features are generated respectively. The user information, the recommendation request, and the multiple preliminary selected contents are sorted according to a preset strategy to generate the user features, the request features, and the multiple content features.

[0051] Sorting is a very important part of the recommendation system and is also the focus of major manufacturers' recommendation systems. Sorting integrates item-side, user-side and context features, and uses a relatively complex model to score a small number of items obtained at the recall layer, with the goal of achieving personalized and accurate recommendations. In addition to focusing on accuracy, the ranking model usually also pays more attention to response speed, which requires that the model structure should not be too complex and the number of features should also be controlled.

[0052] In an embodiment of the present application, key features are extracted through statistical analysis and organization of multiple user information, multiple recommendation requests, and multiple preliminary selected contents, and preset strategies are formulated based on the key features. Each time data is received, the data is organized according to the preset strategy to generate user features, request features, and content features.

[0053] In one embodiment, some features sorted according to the preset strategy are shown in the following table. Due to space limitations, only part of the feature data is shown for illustration, and the feature data used in this application is not limited to this.

[0054]

[0055]

[0056] In S208, the user features, the request features, and the multiple content features are input into an interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents. The interest recommendation model is generated by joint training of a deep recommendation model and a multi-task learning model.

[0057] In one embodiment, the user features, the request features, and the multiple content features can be input into an interest recommendation model; the deep recommendation model part of the interest recommendation model generates a combined feature based on the user features, the request features, and the multiple content features; the multi-task learning model part of the interest recommendation model performs calculations based on the combined feature to generate multiple interest scores corresponding to the multiple content features; wherein the interest score corresponding to the content feature represents the user's interest level in the initially selected content.

[0058] The specific content of "inputting the user characteristics, the request characteristics, and the multiple content characteristics into the interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents" will be described in Figure 5 The corresponding embodiments are described in detail.

[0059] In S210, at least one content to be recommended is determined from the multiple preliminary selected contents based on the multiple interest scores. The multiple preliminary selected contents are sorted based on the multiple interest scores; and at least one preliminary selected content is extracted as the content to be recommended according to the sorting. The N items with the highest scores are taken out according to the comprehensive scores of each content by the user and returned to the client for display to the user.

[0060] According to the content recommendation method of the present application, user information is obtained based on a recommendation request of the user; multiple preliminary selected contents are obtained based on the user information; user features, request features, and multiple content features are respectively generated based on the user information, the recommendation request, and the multiple preliminary selected contents; the user features, the request features, and the multiple content features are input into an interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents, and the interest recommendation model is generated by joint training of a deep recommendation model and a multi-task learning model; based on the multiple interest scores, at least one content to be recommended is determined from the multiple preliminary selected contents, so that the user's interest can be analyzed from multiple angles, and recommended content can be comprehensively generated based on various information, thereby improving recommendation efficiency and user satisfaction.

[0061] It should be clearly understood that the present application describes how to form and use specific examples, but the principles of the present application are not limited to any details of these examples. On the contrary, based on the teaching of the content disclosed in the present application, these principles can be applied to many other embodiments.

[0062] Figure 3 is a schematic diagram of a content recommendation method according to another exemplary embodiment. Figure 3 It is combined with a specific application scenario Figure 2 A detailed description of the process shown.

[0063] In a specific application scenario, the recommendation request is passed from the client to the background server interface. After receiving the request from the client, the server calls the recommendation system interface and passes on the features required by the recommendation algorithm model.

[0064] After receiving the request from the server, the recommendation system will obtain the user portrait and the user vector pre-trained offline and stored in redis according to the user ID. The user vector is a scoring matrix generated based on the user's exposure, clicks, comments, sharing, collections and other behaviors, and then expressed by the user vector generated by matrix decomposition. The recommendation system will also obtain a list of other users with similar interests (similar user portraits) to the user, and recall multiple preliminary content based on the above information.

[0065] After receiving the above recalled content sets, the server first performs exposure elimination, and then retrieves the features of the remaining content from the background redis and MySQL according to the content ID. Then, it requests the interest recommendation model service interface of the recommendation system in protobuf format. The interest recommendation model will return the user's comprehensive score for each content, and then the server retrieves the N items with the highest scores and returns them to the client for display to the user.

[0066] In the embodiments of the present application, transmitting data in protobuf format can speed up the transmission speed, especially for large amounts of data; the protobuf format also supports cross-platform and multi-language, and can also expand the application scope of the solution in the present application.

[0067] Figure 4 The present invention is a flowchart of a content recommendation method according to another exemplary embodiment. Figure 4 The process 40 shown is a detailed description of “generating the interest recommendation model by jointly training the deep recommendation model and the multi-task learning model”.

[0068] like Figure 4 As shown, in S402, an initial model is generated by a deep recommendation model and a multi-task learning model. The DNN hidden layer in the deep recommendation model is set to three layers, each layer has a dropout factor; the output data of the deep recommendation model (DeepFM) is used as the input data of the multi-task learning model; the expert network of the multi-task learning model (mmoe) is set to three, and the expert units are set to sixteen; the DNN hidden layer in the expert network of the multi-task learning model is set to two layers; the initial model is generated based on the above settings.

[0069] In S404, a training sample set is generated based on multiple user recommendation requests. The recommendation requests of multiple users in history can be obtained from the log, and user information, recommendation requests, and multiple content information are extracted based on the records in the log. The above data are feature sorted to generate user features, request features, and content features. A training sample set is generated based on the above data, and each user is used as a training sample.

[0070] In S406, sample labels are set for samples in the training sample set based on the content recommendation results. Log record data is extracted from the log, and labels are set for samples in the training sample set according to the content of subsequent operations of the user. More specifically, multiple labels can be set for the samples, such as click labels and propagation labels.

[0071] In S408, the initial model is trained based on the training sample set data with sample labels, and when the training parameters meet the threshold, the interest recommendation model is generated.

[0072] Specifically, during each training, an initial model is established, and each sample in the training sample set is input into the initial model to obtain a predicted label. The predicted label is compared with the corresponding real label to determine whether the predicted label is consistent with the real label, and the number of predicted labels consistent with the real label is counted, and the proportion of the number of predicted labels consistent with the real label in the number of all predicted labels is calculated. If the proportion is greater than or equal to the preset proportion value, the initial model converges to obtain a trained interest recommendation model. If the proportion is less than the preset proportion value, the parameters in the initial model are adjusted, and the predicted labels of each object are re-predicted by the adjusted initial model until the proportion is greater than or equal to the preset proportion value. Among them, the method of adjusting the parameters in the initial model can be performed using a stochastic gradient descent algorithm, a gradient descent algorithm, or a normal equation.

[0073] In the initial model, the MMoe model cooperates with the DeepFM model, and the output of DeepFM is used as the input of the MMoe model. In the actual model training, it is found that due to the large number of parameters in the depth part of MMoe and DeepFM, it is very easy to have overfitting problems. In the initial model, the depth of the DNN side is initially set to 3 layers, the hidden units are set to [128, 64, 32], the number of expert networks in the MMoe part is 3, the expert units are 16, and the hidden units are [128, 64, 32]. This parameter configuration shows that the auc and acc of the training data are 0.86 and 0.92 respectively during model training, but the performance on the test set is extremely poor (auc is 0.52, acc is 0.6), which is a typical overfitting performance.

[0074] In an embodiment of the present application, L2 regularization penalty and dropout are added to the network layers of DNN and mmoe, wherein the dropout ratio is set to 0.6, and the L2 regularization penalty factor is 0.01. At this time, the auc and acc of the model training are increased to 0.65 and 0.73 respectively, and then the number of expert networks in mmoe is further adjusted to 2 during the training process, and the hidden units are changed to [16,8]. The dropout ratio in the network layer is increased from 0.6 to 0.7. At this time, the performance of the model training for the test set data becomes auc of 0.75 and acc of 0.89. After going online, the ctr is increased by 4.5% without increasing the features.

[0075] Figure 5 is a schematic diagram of a content recommendation method according to another exemplary embodiment. Figure 5 Yes Figure 2 The process S208 shown, "inputting the user characteristics, the request characteristics, and the multiple content characteristics into the interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents" is described in detail.

[0076] In one embodiment, the user features, the request features, and the multiple content features can be input into an interest recommendation model; the deep recommendation model part of the interest recommendation model generates a combined feature based on the user features, the request features, and the multiple content features; the multi-task learning model part of the interest recommendation model performs calculations based on the combined feature to generate multiple interest scores corresponding to the multiple content features; wherein the interest score corresponding to the content feature represents the user's interest level in the initially selected content.

[0077] More specifically, the multi-task learning model part in the interest recommendation model calculates and generates the click probability and the propagation probability based on the combined features; and generates the interest scores corresponding to the multiple content features based on the click probability, the propagation probability and their corresponding weights.

[0078] Among them, the FM model is a machine learning algorithm based on matrix decomposition. The DeepFM model can process many features that traditional models cannot handle. For example, it can directly learn the implicit information of high-dimensional sparse IDs from massive training samples and represent it through Embedding. In addition, for text, sequence features and image features, the deep network has corresponding structures or units to process them.

[0079] In actual calculations, user features, request features, and content features are discretized and input into the DeepFM part of the initial model. After hidden layer calculations, combined features are proposed and input into the MMoe model part. The MMoe model pre-sets various parameters obtained by model training, and the user's click probability and forwarding probability are calculated. Based on these two probabilities and the weights corresponding to the two probabilities, the final interest score value can be obtained.

[0080] Those skilled in the art will appreciate that all or part of the steps for implementing the above embodiments are implemented as a computer program executed by a CPU. When the computer program is executed by the CPU, the above functions defined by the above method provided in the present application are executed. The program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0081] In addition, it should be noted that the above figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0082] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0083] The exemplary embodiments of the present application are specifically shown and described above. It should be understood that the present application is not limited to the detailed structures, configurations or implementations described herein; on the contrary, the present application is intended to cover various modifications and equivalent configurations included in the spirit and scope of the appended claims.

Claims

1. A content recommendation method, characterized in that: include: Obtain user information based on the user's recommendation request; Acquire multiple preliminary selected contents based on the user information; Generate user features, request features, and multiple content features based on the user information, the recommendation request, and the multiple pre-selected contents; Inputting the user feature, the request feature, and the multiple content features into an interest recommendation model to obtain multiple interest scores corresponding to the multiple preliminary selected contents, wherein the interest recommendation model is generated by joint training of a deep recommendation model and a multi-task learning model; determining at least one content to be recommended from the plurality of preliminary selected contents based on the plurality of interest scores; The step of inputting the user feature, the request feature, and the plurality of content features into an interest recommendation model to obtain a plurality of interest scores corresponding to the plurality of preliminary selected contents includes: Inputting the user feature, the request feature, and the plurality of content features into an interest recommendation model; The deep recommendation model part in the interest recommendation model generates a combined feature based on the user feature, the request feature, and the multiple content features; The multi-task learning model part in the interest recommendation model performs calculation based on the combined features to generate multiple interest scores corresponding to the multiple content features; The interest score corresponding to the content feature represents the user's interest in the primary selected content; The multi-task learning model part in the interest recommendation model performs calculation based on the combined features to generate interest scores corresponding to the multiple content features, including: The multi-task learning model part in the interest recommendation model calculates and generates click probability and propagation probability based on the combined features; The interest scores corresponding to the multiple content features are generated based on the click probability, the propagation probability and their corresponding weights.

2. The method according to claim 1, characterized in that Also includes: Generate an initial model through a deep recommendation model and a multi-task learning model; The initial model is trained based on multiple user recommendation requests and multiple content recommendation results to generate the interest recommendation model.

3. The method according to claim 1, characterized in that Acquiring a plurality of preliminary selected contents based on the user information includes: Extracting a pre-stored user portrait and user vector based on the user identifier in the user information; Extracting other users based on the user portrait to generate a list of other users; The plurality of preliminary selected contents are generated based on the user portrait, the user vector, and the other user list.

4. The method according to claim 3, characterized in that Generating the plurality of preliminary selected contents based on the user portrait, the user vector, and the other user list further includes: The multiple preliminary selected contents are exposed and deduplicated based on the user information.

5. The method according to claim 1, characterized in that Generating user features, request features, and multiple content features based on the user information, the recommendation request, and the multiple pre-selected contents respectively includes: The user information, the recommendation request, and the plurality of preliminary selected contents are respectively sorted according to a preset strategy to generate the user feature, the request feature, and the plurality of content features.

6. The method according to claim 1, characterized in that Determining at least one content to be recommended from the plurality of preliminarily selected contents based on the plurality of interest scores includes: sorting the plurality of preliminary selected contents based on the plurality of interest scores; At least one preliminary selected content is extracted according to the ranking as content to be recommended.

7. The method according to claim 2, characterized in that Generate an initial model through a deep recommendation model and a multi-task learning model, including: The DNN hidden layer in the deep recommendation model is set to three layers, each with a dropout factor; Use the output data of the deep recommendation model as the input data of the multi-task learning model; The number of expert networks and expert units of the multi-task learning model is set to two and sixteen respectively; The DNN hidden layer in the expert network of the multi-task learning model is set to two layers; The initial model is generated based on the above settings.

8. The method according to claim 2, characterized in that The initial model is trained based on multiple user recommendation requests and multiple content recommendation results to generate the interest recommendation model, including: Generate a training sample set based on multiple user recommendation requests; Setting sample labels for samples in the training sample set based on the content recommendation result; Training the initial model based on training sample set data with sample labels; When the training parameters meet the threshold, the interest recommendation model is generated.

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