Multi-genre resource recommendation method and device, electronic equipment and storage medium

Abstract

The invention provides a multi-genre resource recommendation method and device, electronic equipment and a storage medium, and relates to the technical fields of resource distribution, resource recommendation, artificial intelligence, large models and the like. According to the specific implementation scheme, the method comprises the steps of obtaining factor values of at least two fusion factors of each body feature resource of at least two body features; based on a pre-learned strategy model, obtaining factor parameters of the fusion factors; on the basis of the factor values and the factor parameters, obtaining comprehensive factor values of the corresponding body cutting resources; and recommending resources to the user based on the comprehensive factor value. According to the technology disclosed by the invention, an effective implementation scheme can be provided for the recommendation of the multi-genuine resources in the immersive recommendation scene, and the recommendation efficiency of the multi-genuine resource recommendation can be effectively ensured.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to technical fields such as resource distribution, resource recommendation, artificial intelligence, and large models, and in particular to a method, device, electronic device, and storage medium for recommending multi-genre resources. Background Art

[0002] Based on the growing demand for consumption efficiency and the popularity of immersive consumption habits, more and more users like to consume resources in an immersive way.

[0003] In the existing immersive recommendation scenario, it is mainly used to recommend video resources. Users enter the immersive recommendation scenario through the video entrance. In the immersive recommendation scenario, users do not need to search independently, but only need to slide up and down, and the immersive recommendation system can continuously recommend videos to users, so that users can consume videos immersively. Moreover, in the existing immersive recommendation scenario, resources can be sorted by content popularity and other factors to achieve resource recommendation. Summary of the invention

[0004] The present disclosure provides a method, device, electronic device and storage medium for recommending multi-genre resources.

[0005] According to one aspect of the present disclosure, a method for recommending multi-genre resources is provided, comprising:

[0006] Obtaining factor values ​​of at least two fusion factors of genre resources of at least two genres;

[0007] Based on the pre-learned strategy model, obtaining factor parameters of each of the fusion factors;

[0008] Based on the factor value and the factor parameter, obtaining the corresponding comprehensive factor value of each of the genre resources;

[0009] Based on the comprehensive factor value, resources are recommended to the user.

[0010] According to another aspect of the present disclosure, a device for recommending multi-genre resources is provided, comprising:

[0011] A factor acquisition module, used for acquiring factor values ​​of at least two fusion factors of genre resources of at least two genres;

[0012] A parameter acquisition module, used for acquiring factor parameters of each fusion factor based on a pre-learned strategy model;

[0013] A comprehensive value acquisition module, used for acquiring the comprehensive factor value of each of the genre resources based on the factor value and the factor parameter;

[0014] The recommendation module is used to recommend resources to users based on the comprehensive factor value.

[0015] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any possible implementation manner and the aspects described above.

[0019] According to yet another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method of the above-mentioned aspect and any possible implementation manner.

[0020] According to yet another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the computer program implements the above-mentioned aspects and any possible implementation method.

[0021] According to the technology disclosed in the present invention, an effective solution can be provided for the recommendation of multi-genre resources in an immersive recommendation scenario, and the recommendation efficiency of the multi-genre resource recommendation can be effectively guaranteed.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0024] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure;

[0025] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure;

[0026] Figure 3 is a schematic diagram according to a third embodiment of the present disclosure;

[0027] Figure 4 is a schematic diagram according to a fourth embodiment of the present disclosure;

[0028] Figure 5 is a schematic diagram according to a fifth embodiment of the present disclosure;

[0029] Figure 6 The block diagram is a block diagram of an electronic device for implementing the method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0031] Obviously, the described embodiments are only part of the embodiments of the present disclosure, but not all of them. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present disclosure.

[0032] It should be noted that the terminal devices involved in the embodiments of the present disclosure may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0033] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0034] Figure 1 is a schematic diagram according to the first embodiment of the present disclosure; Figure 1 As shown, this embodiment provides a method for recommending multi-genre resources, which may specifically include the following steps:

[0035] S101, obtaining factor values ​​of at least two fusion factors of genre resources of at least two genres;

[0036] The execution subject of the method for recommending multi-genre resources in this embodiment may be a multi-genre resource recommendation device, which may be an electronic entity or a software integrated application.

[0037] The method for recommending multi-genre resources of this embodiment can be applied to immersive resource recommendation scenarios. Moreover, in the immersive resource recommendation scenario of this embodiment, resources of multiple genres can be recommended. The resource genre in this embodiment can refer to the style of the resource or the format of the resource.

[0038] The immersive resource recommendation of this embodiment specifically refers to actively and seamlessly recommending several resources to the user without any request from the user. After consuming one resource, the user can directly scroll down and continue to consume the next resource.

[0039] For example, the multiple genres in this embodiment may include video resources and graphic resources. Furthermore, the video resources may also include short video resources and / or small video resources; wherein the playback time of the small video is less than the playback time of the short video. Furthermore, the graphic resources may also include dynamic graphic resources and / or text graphic resources. Dynamic graphic must include pictures and may also include a small amount of text to describe the pictures. Text graphic must include text and may include a small amount of pictures to explain the text.

[0040] In this embodiment, for each resource of each genre, it is necessary to obtain the corresponding factor values ​​of at least two fusion factors; each fusion factor can be considered as a feature corresponding to the resource. In order to comprehensively consider the value of each resource in the future, for each resource, at least two fusion factors, or at least two features, need to be obtained in this implementation.

[0041] S102, based on the pre-learned strategy model, obtaining factor parameters of each fusion factor;

[0042] Specifically, for each genre type of resource, based on the strategy model, the factor parameters of the same fusion factor obtained are the same; for resources of different genre types, the factor parameters of the same fusion factor obtained are different. For example, the strategy model of this embodiment can adopt an evolutionary strategy model, which can be learned offline using a large amount of data, so that it can evolve to the optimal state. When this step is used online, the parameters that have evolved to the optimal state can be obtained, and then the factor parameters of each fusion factor of each genre type of resource can be accurately obtained.

[0043] S103, based on the factor value and the factor parameter, obtaining the corresponding comprehensive factor value of each genre resource;

[0044] For example, for each resource, based on the acquired factor values ​​of at least two fusion factors of the resource and the factor parameters of each fusion factor, a comprehensive factor value of the resource can be obtained by mathematical calculation.

[0045] S104: Recommend resources to the user based on the comprehensive factor value.

[0046] In this embodiment, the comprehensive factor value of each resource can be used to comprehensively represent the recommendation degree of the resource. For example, the higher the comprehensive factor value, the higher the recommendation degree, which means that the resource is more worthy of being recommended.

[0047] The method for recommending multi-genre resources of this embodiment obtains the comprehensive factor value of each resource in multiple genre resources based on the factor value of at least two fusion factors of the resource and the factor parameters of each fusion factor, and then can uniformly recommend various genre resources to users according to the comprehensive factor value, which can provide an effective implementation scheme for the recommendation of multi-genre resources in immersive recommendation scenarios, and can also effectively ensure the recommendation efficiency of multi-genre resource recommendation. Moreover, by adopting the technical scheme of this embodiment, by accurately obtaining the comprehensive factor value of each genre resource, the accuracy of resource sorting can be effectively improved, and the efficiency of resource recommendation can be effectively improved.

[0048] Figure 2 is a schematic diagram according to the second embodiment of the present disclosure; the method for recommending multi-genre resources in this embodiment, in the above Figure 1 Based on the technical solutions of the embodiments shown in the figure, the technical solutions of the present disclosure are further described in more detail. Figure 2 As shown, the method for recommending multi-genre resources in this embodiment may specifically include the following steps:

[0049] S201, obtaining target ranking parameters and first estimated effect characteristics of each genre resource within the corresponding genre type;

[0050] For example, in a resource recommendation scenario of actual application, before the technical solution of this embodiment, resources of various genres are first recalled in the recall layer. Then, after being processed by the coarse and fine ranking layer, the coarse and fine ranking layer may specifically include a coarse ranking layer and a fine ranking layer. Specifically, within the resources of various genre types, multiple resources of the genre type may be coarsely sorted and finely sorted, respectively, and then multiple resources of relatively high quality may be screened out. The technical solution of this embodiment may be considered to put multiple resources of various genre types together for comprehensive sorting after the coarse and fine ranking layer, and then resource recommendations may be made based on the comprehensive sorting results.

[0051] In this embodiment, the target ranking parameter of each resource in the corresponding genre type resource can be considered to be obtained based on the ranking parameter of each resource in the corresponding genre type resource at the fine ranking layer. The ranking parameter may include a ranking score.

[0052] Optionally, in this embodiment, at least two fusion factors may include target ranking parameters of each resource within the resources of the corresponding genre type, and at least one first estimated effect feature. The first estimated effect feature refers to the effect feature that predicts the user when consuming the resource. The user consuming the resource refers to the user browsing or watching the resource, such as the user browsing the video resource, or the graphic resource.

[0053] For example, the first estimated effect feature of each resource in this embodiment may include at least one of the predicted consumption time, fast scrolling probability, interaction probability, completion probability, quality score, decline rate and exit rate of each genre of resources.

[0054] The consumption time indicates the time the user spends on the resource. The longer the consumption time, the higher the user's satisfaction with the resource, and the more worthy the resource is to be recommended to the user; and vice versa.

[0055] The fast sliding probability indicates the probability of a user quickly sliding the resource when consuming the resource. The greater the fast sliding probability, the lower the user's satisfaction with the resource, and the less worthy the resource is to be recommended to the user; vice versa.

[0056] The interaction in this embodiment may include at least one of like, forward, follow, share, and comment, etc. When a user consumes a resource, the greater the probability of interaction, the higher the user's attention to the resource, and the more worthy the resource is to be recommended to the user; and vice versa.

[0057] The completion probability indicates the probability of a complete playback when a user consumes the resource. The greater the completion probability, the higher the user's satisfaction with the resource, and the more worthy the resource is to be recommended to the user; and vice versa.

[0058] The quality score can be considered as a score that comprehensively represents the quality of the resource. The higher the quality score, the better the resource is and the more worthy it is to be recommended to the user, and vice versa. Specifically, the quality score can be obtained by evaluating using a quality evaluation strategy or a quality evaluation model.

[0059] The sliding rate indicates the probability that a user will slide down and consume the next resource after clicking on the current resource. The higher the sliding rate, the higher the user's satisfaction with the current resource, and the more worthy the resource is to be recommended to the user; vice versa.

[0060] The exit rate indicates the probability of a user exiting the current resource. The higher the exit rate, the more dissatisfied the user is with the current resource, and the less worthy the resource is to be recommended to the user; vice versa.

[0061] In actual applications, other estimated effect features may also be included, such as estimated delayed value, etc. The estimated delayed value is used to indicate whether the user will continue to want to watch other resources of the author of the resource after consuming the resource, or whether the user will continue to want to watch other related resources of the resource, such as related content resources, or resources with related tags, etc.

[0062] In this embodiment, each first estimated effect feature can be pre-trained with a corresponding estimation model. When used, the resource feature of the resource, the user feature of the user who wants to consume the resource, the scene feature, and at least one of the entry genre feature of the resource can be input into the estimation model. The estimation model can predict and output the corresponding estimated effect feature based on the input information. Each feature can be represented by a numerical value, where the fast sliding probability, interaction probability, completion probability, quality score, sliding rate, and exit rate can be values ​​between 0 and 1. The higher the value, the greater the probability of the corresponding effect.

[0063] In this embodiment, by obtaining the target ranking parameters of each resource within the resources of the corresponding genre type and at least one of the consumption time, fast scrolling probability, interaction probability, completion probability, quality score, decline rate and exit rate of each resource, at least two fusion factors of each resource are constituted together, which can effectively ensure that the obtained fusion factors are very reasonable and accurate, and provide effective support for the subsequent calculation of the comprehensive factor values ​​of each resource.

[0064] For example, in this embodiment, obtaining the ranking score of each genre resource among multiple genre resources of multiple genres in resources of corresponding genre type may include the following two situations:

[0065] Case 1: The target sorting parameters include the original sorting parameters of the corresponding genre resources in the refined sorting layer within the corresponding genre resource type.

[0066] This approach, i.e., using the original sorting parameters of each resource in the refined sorting layer within the resources of the corresponding genre type as the target sorting parameters of the resource, can effectively ensure the accuracy of the target sorting parameters of the resource.

[0067] Scenario 2: not only the ranking parameters of each resource in the corresponding genre type are considered, but also the satisfaction of each resource is considered; for example, the following steps may be specifically included:

[0068] (1) obtaining original sorting parameters of each genre resource in the refined sorting layer within the corresponding genre type;

[0069] (2) Based on the pre-trained satisfaction prediction model, obtain the satisfaction of resources in each genre;

[0070] (3) Based on the satisfaction and the original ranking parameters, obtain the corresponding ranking parameters of each target;

[0071] For example, in this embodiment, for each genre resource, the product of the original ranking parameter of the genre resource in the resource of the corresponding genre type in the refined ranking layer and the satisfaction level of the resource can be taken as the target ranking parameter of the resource. In this way, by using the satisfaction level of each resource, the ranking parameter of each resource is updated, making the target ranking parameter of the resource more reasonable and accurate.

[0072] S202, normalizing the factor value of the specified type according to the standard of the corresponding resource genre type; the fusion factor of the specified type includes: fusion factors with different resource consideration standards of different genre types;

[0073] In actual application scenarios, the consideration criteria for the factor value of the same fusion factor for resources of different genre types may be different, or the dimensions may be different. For example, for the same fusion factor f1, the value range of the fusion factor in the video genre is 0-150, while in the graphic genre, the value range of the fusion factor is 0-100. The standards of the two are different. If the comprehensive factor value of the resource is calculated according to their respective values, it will lead to unfair and inaccurate recommendations for resources of various genre types. Therefore, in order to solve this problem, in this embodiment, the factor values ​​of fusion factors of resources of different genre types and different consideration criteria can be normalized according to the standards of the genre of the corresponding resource.

[0074] For example, for the fusion factor f1 of the video genre, all values ​​within the range of 0-150 are normalized to between 0-1. For the fusion factor f1 of the graphic genre, all values ​​within the range of 0-100 are also normalized to between 0-1. In this way, the standards of resources of different genres are unified for the same fusion factor f1, so that the standards of the same fusion factor of resources of different genre types are the same, and then the comprehensive factor values ​​of various genre resources can be calculated fairly, reasonably and accurately, and multi-genre resource recommendations can be achieved in a fair, reasonable and accurate manner.

[0075] S203, based on the pre-learned Evolution Strategies (ES) model, obtaining factor parameters of each fusion factor of various genre resources;

[0076] In this embodiment, a pre-learned Covariance Matrix Adaptation (CMA) ES algorithm may be specifically used to obtain factor parameters of each fusion factor of various genre resources.

[0077] Specifically, in at least two fusion factors of each genre resource, the factor parameters of the same fusion factor are the same, and in at least two fusion factors of different genre resources, the factor parameters of the same fusion factor are different.

[0078] For the factor parameters of at least two fusion factors of each genre resource, the evolution strategy model can be pre-learned offline based on historical traffic. During the learning process, for each genre resource, multiple sets of parameters of at least two fusion factors can be configured, and then traffic is allocated to each set of parameters to detect the effect of each set of parameters. For example, for various parameters, the total consumption time brought by the set of parameters can be used as the effect of the set of parameters. Then evolve each set of parameters so that each set of parameters is adjusted in the direction of better effect. After rounds of iterative learning and evolution of multiple sets of parameters, the optimal parameters of at least two fusion factors can be obtained as factor parameters.

[0079] Optionally, in an embodiment of the present disclosure, the parameters of each fusion factor of various genre resources may include a factor index of each fusion factor.

[0080] Further optionally, in the case where the genre resources include graphic and text resources, the factor parameter of the fusion factor may also include a bias value.

[0081] That is, when the step S203 is implemented specifically, it may include the following steps: based on the evolution strategy model, obtaining the index of each fusion factor of various genre resources. The index here refers to the power. For example, f1 a In the equation, f1 represents the fusion factor, and a represents the exponent of the fusion factor; or f1^a can be used to represent the relationship.

[0082] Furthermore, when the resource genre includes graphic and text resources, the bias values ​​of the fusion factors of the graphic and text resources can also be obtained based on the evolution strategy model.

[0083] Specifically, in the traditional immersive resource recommendation scenario, resources of video genres are mainly recommended. In the immersive resource recommendation scenario of this embodiment, resources of multiple genres can be recommended, for example, resources of video genres and resources of graphic genres can be recommended. Compared with resources of video genres, resources of graphic genres are newly added genres in this embodiment. In order to enable resources of different genres to be put together and sorted more objectively, fairly and accurately, in this embodiment, bias values ​​can be set for each fusion factor of graphic resources. Specifically, the bias values ​​of each fusion factor of graphic resources can also be pre-learned through the evolutionary strategy model.

[0084] In addition, in one embodiment of the present disclosure, if the resources of multiple genres include resources of text-graphic genre and resources of dynamic-graphic genre, the resources of text-graphic genre and resources of dynamic-graphic genre need to be pre-learned through the evolution strategy model respectively.

[0085] S204: for each resource among the multiple resources of multiple genres, based on the factor values ​​of each fusion factor corresponding to the resource and the factor parameters of each fusion factor, obtain a comprehensive factor value of the resource;

[0086] Specifically, for a resource of a video genre, the comprehensive factor value of the resource may be acquired according to the factor values ​​of at least two fusion factors of the resource and the index of each fusion factor.

[0087] For example, a resource includes f1, f2, and f3, and three fusion factors. The index of fusion factor f1 is a1, the index of fusion factor f2 is a2, and the index of fusion factor f3 is a3. At this time, the comprehensive score of the resource can be expressed by the following formula:

[0088] S=f1 a1 *f2 a2 *f3 a3

[0089] Specifically, for a resource of graphic and text genre, the comprehensive factor value of the resource may be obtained according to the factor values ​​of at least two fusion factors of the resource, the index of each fusion factor, and the bias value of each fusion factor.

[0090] For example, a resource includes f1, f2, and f3, and three fusion factors. The index of fusion factor f1 is a1, the index of fusion factor f2 is a2, the index of fusion factor f3 is a3, the bias value of fusion factor f1 is b1, the index of fusion factor f2 is b2, and the index of fusion factor f3 is b3. At this time, the comprehensive score of the resource can be expressed by the following formula:

[0091] S=(f1-b1) a1 *(f2-b2) a2 *(f3-b3) a3

[0092] S205, sorting multiple resources of multiple genres according to comprehensive factor values;

[0093] S206: Based on the ranking of the comprehensive factor values ​​of multiple resources of multiple genres, obtain N top-ranked resources and recommend immersive resources to the user.

[0094] Steps S205-S206 of this embodiment are as described above. Figure 1A specific implementation of step S104 in the illustrated embodiment. The number N can be set based on the requirements of the immersive resource recommendation scenario, for example, it can be 6 or 8, etc., which is not limited here.

[0095] The method for recommending multi-genre resources of this embodiment, by adopting the above-mentioned technical solution, can normalize the factor values ​​of fusion factors of resources of different genres and different consideration criteria in the resources of each genre type, so that the standards of the values ​​of each fusion factor of resources of different genre types are unified, and thus it is possible to make subsequent multi-genre resource recommendations more reasonably, fairly, accurately and effectively. Moreover, by adopting the above-mentioned technical solution, it is also possible to accurately and efficiently calculate the comprehensive factor value of each resource, and then more accurately recommend multi-genre resources based on the ranking of the comprehensive factor values ​​of each resource. The technical solution of this embodiment can further effectively improve the accuracy of resource ranking, and thus can effectively improve the efficiency of resource recommendation.

[0096] Figure 3 is a schematic diagram according to the third embodiment of the present disclosure; the method for recommending multi-genre resources in this embodiment, in the above Figure 2 Based on the technical solutions of the embodiments shown in the figure, the technical solutions of the present disclosure are further described in more detail. Figure 3 As shown, this embodiment provides a Figure 2 The specific implementation method of "predicting the satisfaction of each resource based on the pre-trained satisfaction prediction model" in the illustrated embodiment may include the following steps:

[0097] S301, obtaining user characteristics of the user;

[0098] In this embodiment, the acquisition and use of user characteristics are known and agreed by the user.

[0099] For example, when this step is specifically implemented, it may include: obtaining at least one of the attribute characteristics of the user and the historical consumption characteristics of the user;

[0100] The attribute characteristics of the user include: at least one of the basic attribute characteristics of the user and the preference characteristics of the user; the basic attribute characteristics of the user include the user's age, gender, occupation type, etc. The preference characteristics of the user include the user's interests and hobbies, etc. The preference characteristics of the user can be identified in the user's attribute information in the form of tags, for example, interests and hobbies can include travel, entertainment, football, etc. The attribute characteristics of the user have a certain correlation with the resources, which can provide a reference for predicting the user's satisfaction with the resources. For example, different ages will have different resource preferences, for example, the middle-aged and elderly like to consume video resources; middle-aged office workers like to consume graphic resources; middle-aged women like to consume fashion resources, while middle-aged men like to consume financial resources; and the elderly like to consume health resources, and so on.

[0101] The historical consumption characteristics of the user include at least one of the consumption proportion of resources of various genres consumed by the user in multiple historical time periods before the current period, the duration of consumption of resources of various genres, and the completion rate of resources of various genres. If the genre of the currently predicted resource belongs to the resource genre with a relatively high consumption proportion in multiple historical time periods, the corresponding satisfaction will be higher. On the contrary, if the genre of the currently predicted resource belongs to the resource genre with the lowest consumption proportion in multiple historical time periods, the corresponding satisfaction will be lower. Similarly, if the genre of the currently predicted resource belongs to the resource with a relatively high completion rate in multiple historical time periods, the corresponding satisfaction will be higher. On the contrary, if the genre of the currently predicted resource belongs to the resource with a relatively low completion rate in multiple historical time periods, the corresponding satisfaction will be lower.

[0102] The length of the current time period can be a preset time length defined in the resource recommendation scenario, such as the current time period can be the current day, the current week, etc. Multiple historical time periods can be configured according to demand, for example, including historical one day, historical three days, historical seven days, etc. The length and number of specific historical time periods are not limited here. The consumption proportion of users consuming resources of various genres, the duration of consumption of resources of various genres, and the completion rate of resources of various genres in each historical time period can be obtained by statistically analyzing the historical consumption information of the user.

[0103] Further optionally, in this embodiment, a scene feature can also be obtained, and the scene feature can identify whether the current immersive resource recommendation scene belongs to a recommendation scene or a discovery scene. The recommendation scene and the discovery scene of this embodiment can be two different sections in the application. Different users may have different scene preferences. Based on the scene feature, the accuracy of the predicted user satisfaction with the resource can be improved.

[0104] S302: For each resource, obtain resource characteristics of the resource;

[0105] For example, the resource characteristics of the resource may include: at least one of: resource length, resource identifier, resource tag, resource genre type, resource entry genre, and a second estimated effect characteristic of the resource;

[0106] When the resource genre type is video resource, the resource length refers to the duration of the video; when the resource genre type is graphic resource, the resource length refers to the length of the text and the number of pictures included in the resource;

[0107] The resource entry genre feature of this embodiment may refer to the genre type feature of the resource entry resource, which can identify the genre type of the resource entry resource as video or graphic;

[0108] The second estimated effect feature of the resource in this embodiment may include at least one of the predicted resource consumption time, fast scrolling probability, interaction probability, completion probability, quality score, decline rate and exit rate.

[0109] Specifically, the second estimated effect feature of the resource may also include an estimated deferred value, which is used to characterize whether the user will continue to want to watch other resources of the author of the resource after consuming the resource, or whether the user will continue to want to watch other related resources of the resource; such as related content resources, or resources with related tags, etc.

[0110] S303: For each resource, based on the user characteristics of the user and the resource characteristics of the resource, a pre-trained satisfaction prediction model is used to predict the user's satisfaction with the resource as the satisfaction of the corresponding resource.

[0111] When making a specific prediction, for each resource, the resource characteristics of the resource and the user characteristics of the user are input into the satisfaction prediction model, and the satisfaction prediction model can predict and output a satisfaction level. In practical applications, the satisfaction level can be a value between 0 and 1. The larger the value, the higher the satisfaction level of the resource and the more worthy of recommendation.

[0112] Optionally, if scenario features are obtained at the same time, the scenario features, together with resource features and user features, are input into the satisfaction prediction model to improve the accuracy of the satisfaction predicted by the satisfaction prediction model.

[0113] The resource satisfaction estimation method of this embodiment can use a satisfaction estimation model to efficiently and accurately estimate user satisfaction with resources, providing effective support for resource recommendation. Furthermore, the accurate resource satisfaction obtained by this embodiment can further effectively improve the accuracy of resource sorting, thereby effectively improving the efficiency of resource recommendation.

[0114] Moreover, in this embodiment, the user characteristics of users and the resource characteristics of resources used in the satisfaction estimation are very rich, which can effectively improve the accuracy of the satisfaction estimation.

[0115] Figure 4 is a schematic diagram according to a fourth embodiment of the present disclosure; this embodiment provides a device 400 for recommending multi-genre resources, including:

[0116] A factor acquisition module 401 is used to acquire factor values ​​of at least two fusion factors of genre resources of at least two genres;

[0117] A parameter acquisition module 402 is used to acquire factor parameters of each fusion factor based on a pre-learned strategy model;

[0118] A comprehensive value acquisition module 403, configured to acquire the comprehensive factor value of each of the genre resources based on the factor value and the factor parameter;

[0119] The recommendation module 404 is used to recommend resources to the user based on the comprehensive factor value.

[0120] The device 400 for recommending multi-genre resources of this embodiment realizes the implementation principle and technical effect of recommending multi-genre resources by adopting the above modules, which is the same as the implementation of the above related method embodiments. For details, please refer to the records of the above related method embodiments, which will not be repeated here.

[0121] Figure 5 is a schematic diagram of a fifth embodiment of the present disclosure; the device 500 for recommending multi-genre resources of this embodiment, in the above Figure 4 Based on the technical solutions of the embodiments shown in the figure, the technical solutions of the present disclosure are further described in more detail. Figure 5 As shown, the multi-genre resource recommendation device 500 of this embodiment includes the above Figure 4 The modules with the same name and function are shown as: factor acquisition module 501 , parameter acquisition module 502 , comprehensive value acquisition module 503 , and recommendation module 504 .

[0122] The factor acquisition module 501 is used to:

[0123] Obtaining a target ranking parameter and a first estimated effect feature of each of the genre resources within the corresponding genre type;

[0124] Among them, the first estimated effect feature includes: at least one of the predicted consumption time, fast scrolling probability, interaction probability, completion probability, quality score, decline rate and exit rate of each of the genre resources.

[0125] Further optionally, in one embodiment of the present disclosure, the factor acquisition module 501 is used to:

[0126] Obtaining original sorting parameters of each genre resource in the refined sorting layer within the corresponding genre type;

[0127] Based on a pre-trained satisfaction prediction model, obtaining the satisfaction of each of the genre resources;

[0128] Based on the satisfaction level and the original ranking parameters, the corresponding target ranking parameters are obtained.

[0129] Further optionally, in one embodiment of the present disclosure, the factor acquisition module 501 is used to:

[0130] Obtaining user characteristics of the user;

[0131] Obtain resource characteristics of each of the genre resources;

[0132] Based on the user characteristics and the resource characteristics, the satisfaction degree corresponding to the genre resource is acquired by using the satisfaction degree prediction model.

[0133] Further optionally, in an embodiment of the present disclosure, the user characteristics include: at least one of an attribute characteristic and a historical consumption characteristic;

[0134] The attribute characteristics include: at least one of basic attribute characteristics and preference characteristics; the historical consumption characteristics include at least one of the consumption proportion of resources of various genres consumed by users in at least two historical time periods before the current time period, the duration of consumption of resources of various genres, and the completion rate of resources of various genres.

[0135] Further optionally, in an embodiment of the present disclosure, the resource feature includes: at least one of resource length, resource identifier, resource tag, resource genre type, resource entry genre, and second estimated effect feature;

[0136] In the case where the resource genre type includes a video type, the resource length includes the duration of the video;

[0137] In the case that the resource genre type includes a picture and text type, the resource length includes the length of the text in the picture and text and the number of pictures.

[0138] Further optionally, if Figure 5 As shown, in an embodiment, the device 500 for recommending multi-genre resources further includes:

[0139] The normalization module 505 is used to normalize the factor value of the specified type according to the standard of the corresponding resource genre type. The fusion factor of the specified type includes: fusion factors with different resource consideration standards of different genre types.

[0140] Further optionally, in one embodiment of the present disclosure, the factor parameter includes a factor index.

[0141] Further optionally, in an embodiment of the present disclosure, when the genre resource includes a graphic resource, the factor parameter includes a bias value.

[0142] The device 500 for recommending multi-genre resources of this embodiment realizes the implementation principle and technical effect of recommending multi-genre resources by adopting the above modules, which is the same as the implementation of the above related method embodiments. For details, please refer to the records of the above related method embodiments, which will not be repeated here.

[0143] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0144] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0145] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0146] like Figure 6 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0147] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0148] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as the above-mentioned methods of the present disclosure. For example, in some embodiments, the above-mentioned methods of the present disclosure may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the above-mentioned methods of the present disclosure described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the above-mentioned methods of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0149] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0150] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0151] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0152] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0153] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0154] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0155] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0156] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for recommending multi-genre resources, comprising: Obtaining factor values ​​of at least two fusion factors of genre resources of at least two genres; Based on the pre-learned strategy model, obtaining factor parameters of each of the fusion factors; Based on the factor value and the factor parameter, obtaining the corresponding comprehensive factor value of each of the genre resources; Based on the comprehensive factor value, resources are recommended to the user.

2. The method according to claim 1, wherein: The step of obtaining the factor values ​​of at least two fusion factors of the genre resources of at least two genres includes: Obtaining a target ranking parameter and a first estimated effect feature of each of the genre resources within the corresponding genre type; Among them, the first estimated effect feature includes: at least one of the predicted consumption time, fast scrolling probability, interaction probability, completion probability, quality score, decline rate and exit rate of each of the genre resources.

3. The method according to claim 2, wherein: The target sorting parameters include original sorting parameters of the corresponding genre resources in the refined sorting layer within the corresponding genre resource type.

4. The method according to claim 2, wherein: The obtaining of target sorting parameters of each genre resource within a corresponding genre type includes: Obtaining original sorting parameters of each genre resource in the refined sorting layer within the corresponding genre type; Based on a pre-trained satisfaction prediction model, obtaining the satisfaction of each of the genre resources; Based on the satisfaction level and the original ranking parameters, the corresponding target ranking parameters are obtained.

5. The method according to claim 4, wherein: The obtaining of the satisfaction of each of the genre resources based on the pre-trained satisfaction prediction model includes: Obtaining user characteristics of the user; Obtain resource characteristics of each of the genre resources; Based on the user characteristics and the resource characteristics, the satisfaction degree corresponding to the genre resource is acquired by using the satisfaction degree prediction model.

6. The method according to claim 5, wherein: The user characteristics include: at least one of attribute characteristics and historical consumption characteristics; The attribute characteristics include: at least one of basic attribute characteristics and preference characteristics; the historical consumption characteristics include at least one of the consumption proportion of resources of various genres consumed by users in at least two historical time periods before the current time period, the duration of consumption of resources of various genres, and the completion rate of resources of various genres.

7. The method according to claim 5, wherein: The resource feature includes: at least one of a resource length, a resource identifier, a resource tag, a resource genre type, a resource entry genre, and a second estimated effect feature; In the case where the resource genre type includes a video type, the resource length includes the duration of the video; In the case that the resource genre type includes a picture and text type, the resource length includes the length of the text in the picture and text and the number of pictures.

8. The method according to claim 1, wherein: After obtaining the factor values ​​of at least two fusion factors of each genre resource of at least two genres, and before obtaining the corresponding comprehensive factor value of each genre resource based on the factor value and the factor parameter, the method further includes: The factor value of the specified type is normalized according to the standard of the corresponding resource genre type, and the fusion factor of the specified type includes: fusion factors with different resource consideration standards of different genre types.

9. The method according to any one of claims 1 to 8, wherein: The factor parameters include factor indexes.

10. The method according to claim 9, wherein: In the case where the genre resource includes a graphic resource, the factor parameter includes a bias value.

11. A device for recommending multi-genre resources, comprising: A factor acquisition module, used for acquiring factor values ​​of at least two fusion factors of genre resources of at least two genres; A parameter acquisition module, used for acquiring factor parameters of each fusion factor based on a pre-learned strategy model; A comprehensive value acquisition module, used for acquiring the comprehensive factor value of each of the genre resources based on the factor value and the factor parameter; The recommendation module is used to recommend resources to users based on the comprehensive factor value.

12. The device according to claim 11, wherein The factor acquisition module is used to: Obtaining a target ranking parameter and a first estimated effect feature of each of the genre resources within the corresponding genre type; Among them, the first estimated effect feature includes: at least one of the predicted consumption time, fast scrolling probability, interaction probability, completion probability, quality score, decline rate and exit rate of each of the genre resources.

13. The device according to claim 12, wherein: The target sorting parameters include original sorting parameters of the corresponding genre resources in the refined sorting layer within the corresponding genre resource type.

14. The device according to claim 12, wherein: The factor acquisition module is used to: Obtaining original sorting parameters of each genre resource in the refined sorting layer within the corresponding genre type; Based on a pre-trained satisfaction prediction model, obtaining the satisfaction of each of the genre resources; Based on the satisfaction level and the original ranking parameters, the corresponding target ranking parameters are obtained.

15. The device according to claim 14, wherein: The factor acquisition module is used to: Obtaining user characteristics of the user; Obtain resource characteristics of each of the genre resources; Based on the user characteristics and the resource characteristics, the satisfaction degree corresponding to the genre resource is acquired by using the satisfaction degree prediction model.

16. The device according to claim 15, wherein: The user characteristics include: at least one of attribute characteristics and historical consumption characteristics; The attribute characteristics include: at least one of basic attribute characteristics and preference characteristics; the historical consumption characteristics include at least one of the consumption proportion of resources of various genres consumed by users in at least two historical time periods before the current time period, the duration of consumption of resources of various genres, and the completion rate of resources of various genres.

17. The device according to claim 15, wherein: The resource feature includes: at least one of a resource length, a resource identifier, a resource tag, a resource genre type, a resource entry genre, and a second estimated effect feature; In the case where the resource genre type includes a video type, the resource length includes the duration of the video; In the case that the resource genre type includes a picture and text type, the resource length includes the length of the text in the picture and text and the number of pictures.

18. The device according to claim 11, wherein The device also includes: The normalization module is used to normalize the factor value of the specified type according to the standard of the corresponding resource genre type, and the fusion factor of the specified type includes: fusion factors with different resource consideration standards of different genre types.

19. The device according to any one of claims 11 to 18, wherein: The factor parameters include factor indexes.

20. The device according to claim 19, wherein In the case where the genre resource includes a graphic resource, the factor parameter includes a bias value.

21. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 10.

22. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-10.

23. 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 10.

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