Shared content list generation method and device for team, equipment and medium

Abstract

The invention relates to a shared content list generation method for a team, and the method comprises the steps: randomly extracting a to-be-calculated content from a content database, and obtaining a content label of the to-be-calculated content; calculating to obtain a personal preference value according to the personal behavior data of each dimension feature; calculating a team preference value according to the team behavior data of each dimension feature; calculating a preference matching value of the to-be-calculated content according to the personal preference value, the team preference value and the corresponding weight value; and when the preference matching value is greater than the preference matching threshold value, adding the content to be calculated into the shared content list. By combining the preference data of individuals and teams, the content that members may be interested in can be more accurately predicted and recommended.

Description

Technical Field

[0001] The present application relates to the technical field of content recommendation, and in particular to a method, apparatus, computing device, and computer-readable storage medium for generating a shared content list for a team. Background Art

[0002] In modern team collaboration, the construction and management of shared content lists is one of the key factors in improving team collaboration and overall work efficiency. Team members often need to share a large amount of content, such as video, audio and other materials, for learning, communication and cooperation. These shared contents not only help improve the skills of members, but also ensure that team members can obtain the latest information synchronously, maintaining the consistency and efficiency of collaboration.

[0003] However, the current shared content recommendation system fails to fully consider the common needs of team members, resulting in the recommended content being too focused on a specific direction or category, or ignoring the interests and needs of other team members. The singleness and inaccuracy of this content recommendation makes the content in the shared content list unable to truly reflect the overall needs of the team, reducing the value of the shared content. Therefore, how to accurately generate a shared content list that is recognized by all team members is still a problem that needs to be solved. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present application proposes a method for generating a shared content list for a team, comprising: randomly extracting a content to be calculated from a content database, and obtaining a content tag of the content to be calculated, wherein the content tag is used to describe multiple dimensional features to which the content to be calculated belongs; calculating a personal preference value based on personal behavior data of each dimensional feature, wherein the personal behavior data is used to represent: the interaction behavior data between individual users in the system and the content in the content database, wherein the personal preference value is used to represent the popularity of the dimensional feature to which the content belongs among the individual user group; calculating a team preference value based on team behavior data of each dimensional feature, wherein the team behavior data is used to represent: the interaction behavior data between all members of the team and the content in the shared content list, wherein the team preference value is used to represent the popularity of the dimensional feature to which the content belongs among the team members; calculating a preference matching value of the content to be calculated based on the personal preference value, the team preference value and the corresponding weight value, wherein the preference matching value is used to represent the popularity of the content among individual users and team members; when the preference matching value is greater than the preference matching threshold, adding the content to be calculated to the shared content list.

[0005] The method described above, which calculates the personal preference value based on the personal behavior data of each dimensional feature, includes: counting the personal behavior data of the type to be calculated in each dimensional feature; counting the sum of all types of personal behavior data in each dimensional feature; obtaining the personal characteristic preference value of each dimensional feature based on the ratio of the personal behavior data of the type to be calculated in each dimensional feature to the sum of all types of personal behavior data, wherein the personal characteristic preference value is used to indicate the popularity of the type to which the content belongs among the individual user group; and obtaining the personal preference value by performing geometric mean calculation based on the personal characteristic preference values ​​of all dimensional features.

[0006] The method described above, obtaining the personal feature preference value of each dimensional feature according to the ratio of the personal behavior data of the type to be calculated content to the sum of all types of personal behavior data in each dimensional feature, includes: obtaining the i-th personal behavior data feature preference value in each dimensional feature according to the ratio of the i-th personal behavior data of the type to be calculated content to the sum of all types of personal behavior data, where i∈c, and there are a total of c types of personal behavior data; and weighted normalizing the c types of personal behavior data feature preference values ​​to obtain the personal feature preference value of each dimensional feature.

[0007] The method described above, which calculates the team preference value based on the team behavior data of each dimensional feature, includes: counting the team behavior data of the type to be calculated in each dimensional feature; counting the sum of all types of team behavior data in each dimensional feature; obtaining the team feature preference value of each dimensional feature based on the ratio of the team behavior data of the type to be calculated in each dimensional feature to the sum of all types of team behavior data, wherein the team feature preference value is used to indicate the popularity of the type to which the content belongs among team members; and obtaining the team preference value by performing geometric mean calculation on the team feature preference values ​​of all dimensional features.

[0008] The method described above, obtaining the team feature preference value of each dimensional feature according to the ratio of the team behavior data of the type to which the content to be calculated belongs in each dimensional feature to the sum of the team behavior data of all types includes: obtaining the Ith type of team behavior data feature preference value in each dimensional feature according to the ratio of the Ith type of team behavior data of the type to which the content to be calculated belongs in each dimensional feature to the sum of the Ith type of team behavior data of all types, where I∈C, and there are a total of C types of team behavior data; and weighted normalizing the C types of team behavior data feature preference values ​​to obtain the team feature preference value of each dimensional feature.

[0009] According to the method described above, the team preference value is calculated based on the team behavior data of each dimension feature and the behavior data of team members on the shared content list.

[0010] Using the above method, the team preference value is calculated using the following formula:

[0011] Among them, score_g is the team preference value, B k is the team behavior data feature preference value of the kth dimension feature. There are a total of z dimension features. α I is the weight value of the I-th type of team behavior data, there are s types of team behavior data, X is the number of shared content lists, sum(v Ix ) is the I-th type of team behavior data of the type of content to be calculated in the x-th shared content list, sum(v_all Ix ) is the sum of the first type of team behavior data of all types in the xth shared content list, is a weighting coefficient of the shared content list x, and the weighting coefficient is calculated according to the behavior data of the team members on the shared content list.

[0012] According to the above method, the behavior data of the team members on the shared content list includes: the click data of the team members on the shared content list, the number of shares, and whether to delete one or more items in the shared content list.

[0013] According to the method described above, a personal preference value is calculated based on the personal behavior data of each dimensional feature within multiple specified time periods; and / or a team preference value is calculated based on the team behavior data of each dimensional feature within multiple specified time periods and the behavior data of team members on the shared content list.

[0014] According to the method described above, the individual users in the system include: all users registered in the system, users who have joined a team among all users registered in the system, and any one of users who have joined a target team among all users registered in the system.

[0015] According to the method described above, the content includes one or more of a dance video and dance music, and the dance music is a song separated from the dance video.

[0016] According to the method described above, when the content to be calculated is a dance video or dance music, the dimensional features of the dance video or dance music are decomposed into one or more of dance type features, action type features and rhythm type features.

[0017] According to another aspect of the present application, a device for generating a shared content list for a team is proposed, comprising: an extraction module, which is used to randomly extract a content to be calculated in a content database and obtain a content tag of the content to be calculated, wherein the content tag is used to describe multiple dimensional features to which the content to be calculated belongs; a first calculation module, which is used to calculate a personal preference value based on personal behavior data of each dimensional feature, wherein the personal behavior data is used to represent: interactive behavior data between individual users in the system and content in the content database, wherein the personal preference value is used to represent the popularity of the content among individual user groups; a second calculation module, which is used to calculate a team preference value based on team behavior data of each dimensional feature, wherein the team behavior data is used to represent: interactive behavior data between all members of the team and content in the shared content list, wherein the team preference value is used to represent the popularity of the content among team members; a third calculation module, which is used to calculate a preference matching value of the content to be calculated based on the personal preference value, the team preference value and the corresponding weight value, wherein the preference matching value is used to represent the popularity of the content among individual users and team members; and a judgment module, which is used to add the content to be calculated to the shared content list when the preference matching value is greater than a preference matching threshold.

[0018] According to another aspect of the present application, a computing device is provided, including: a processor; and a memory storing a computer program, wherein when the computer program is executed by the processor, the method for generating a shared content list for a team as described above is implemented.

[0019] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for generating a shared content list for a team as described above is implemented.

[0020] When judging whether to add the content to be calculated to the shared content list, this application refers to the personal user behavior data and promptly adds popular content to the shared content list to avoid simply "one size fits all" recommendations, which makes the content type too single. Furthermore, when judging whether to add the content to be calculated to the shared content list, the behavior data of each member of the team is also considered, so that the content in the shared content list conforms to the common interests of team members, which is conducive to promoting interaction and cooperation within the team. By combining personal and team preference data, this application can more accurately predict and recommend content that members may be interested in. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Below, the preferred embodiments of the present application will be further described in detail with reference to the accompanying drawings, wherein: Figure 1 It is a flow chart of a method for generating a shared content list for a team according to an embodiment of the present application.

[0022] Figure 2 This is a flow chart of the method for calculating the personal preference value in step S120.

[0023] Figure 3 This is a flow chart of the method for calculating the team preference value in step S130.

[0024] Figure 4 This is a single interface diagram of a shared dance video list and shared dance music according to an embodiment of the present application.

[0025] Figure 5 The invention is a device for generating a shared content list for a team according to an embodiment of the present application.

[0026] Figure 6 It is a structural principle block diagram of a computing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In the following detailed description, reference may be made to the various specification drawings that are part of the present application and are used to illustrate specific embodiments of the present application. In the accompanying drawings, similar reference numerals describe substantially similar components in different figures. The various specific embodiments of the present application are described below in sufficient detail so that a person of ordinary skill in the art with relevant knowledge and skills in the art can implement the technical solutions of the present application. It should be understood that other embodiments may also be used or structural, logical or electrical changes may be made to the embodiments of the present application.

[0029] The shared content list of this application is a list of various content that can be shared and accessed. In the context of teamwork or project management, it is usually used to indicate the materials, files, videos, audio or other forms of content that can be viewed, downloaded or used by all team members. For example, in a dance team, there may be a "shared content list" that lists all dance videos, dance music or rehearsal materials, and team members can access and use these contents as needed. The shared content list can be in digital form, such as stored in the resource library of the application or in a database on a cloud server.

[0030] When the team is a dance team, the content to be calculated in this application includes one or more of dance videos and dance music. Dance videos are audio and video files that record dance demonstrations, usually including dance moves and dancers' performances. These videos may be used as rehearsal references, teaching materials, or performance playbacks.

[0031] Dance music refers to music that goes with the dance performed in a dance video. Dance music can be songs that are "separated" from a dance video. In other words, the original audio track in the dance video contains music, and these audio tracks (i.e. dance music) are extracted or obtained separately from the video and added to the shared content list for use in practice, performance or other related activities.

[0032] Figure 1 FIG. 1 is a flow chart of a method for generating a shared content list for a team according to an embodiment of the present application. Figure 1 As shown, the method includes: Step S110, randomly extracting a content to be calculated from the content database, and obtaining a content tag of the content to be calculated, wherein the content tag is used to describe multiple dimensional features of the content to be calculated; Step S120, calculating a personal preference value based on the personal behavior data of each dimensional feature, wherein the personal behavior data is used to represent: the interaction behavior data between the individual user in the system and the content in the content database, and the personal preference value is used to represent the popularity of the dimensional feature to which the content belongs in the individual user group; Step S130, calculating a team preference value based on the team behavior data of each dimensional feature, wherein the team behavior data is used to represent: interaction behavior data of all members in the team with the content in the shared content list, and the team preference value is used to represent the popularity of the dimensional feature to which the content belongs among the team members; Step S140, calculating the preference matching value of the content to be calculated according to the personal preference value, the team preference value and the corresponding weight value, wherein the preference matching value is used to indicate the popularity of the content among the individual user and the team member; Step S150: When the preference matching value is greater than the preference matching threshold, the content to be calculated is added to the shared content list.

[0033] In step S110, a content to be calculated is randomly selected from the content database. The content database may contain different types of content, such as dance videos, dance music, dance tutorials, etc. Each content item has one or more content tags, which are used to describe its characteristics in multiple dimensions, such as the type, style, difficulty, duration, music type, etc. of the content. For example, when the content to be calculated is a dance video, the content tags of a video may include multiple dimensional features such as "street dance", "basic movements", "3 minutes" and "dynamic music".

[0034] In step S120, personal behavior data of the content to be calculated in multiple dimensions is obtained and the personal preference value is calculated. According to the previous calculation method, the calculation is performed directly based on the personal behavior data of the content to be calculated. Different from this, the present application does not focus on whether the content is liked by the individual user group, but whether the content tag represented by the content (that is, the type of the content) is liked by the individual user group, thereby obtaining the type of content that the individual user group likes. Interactive behavior data refers to data obtained from statistics of user operations on content, such as one or more of the number of times the content is played, the length of time it is played, the number of likes, the number of favorites, and the number of shares. In this application, individual users can be any of the following three groups of people: ① All registered users in the system: This group includes users who have not joined any team and users who have joined some teams. The system will analyze the behavior data of all users in order to obtain the preference information of all users in the system; ② Users who have joined a team among all registered users in the system: This group refers to all users who have registered in the system and joined at least one team. The behavioral data of this type of users can reflect their preferences in different teams or collective environments; ③ Users who have joined the target team among all registered users in the system: This group specifically refers to users who have registered and joined the target team. For example, the target team users refer to the team corresponding to the shared content list, and the behavior data of these users will be used to calculate the personal preference values ​​of the members of the team.

[0035] In step S130, the team behavior data of the content to be calculated in multiple dimensions is obtained and the team preference value is calculated. The calculation method of the team preference value is similar to the individual preference value, but it is based on the interaction behavior data of the entire team members instead of the data of a single user.

[0036] In step S140, the preference matching value of the content to be calculated is calculated by combining the personal preference value and the team preference value and according to the set weight value. The weight value is used to measure the importance of personal preference and team preference in the final decision. In some embodiments, the weight value of the team preference value is greater than the weight value of the personal preference value to focus on whether the content to be calculated is liked by team members.

[0037] In step S150, a determination is made as to whether to add the content to be calculated to the shared content list according to a preset preference matching threshold. If the preference matching value of the content to be calculated is greater than the threshold, the content is added to the shared content list for access and use by team members.

[0038] In the scheme of the present application, personal user behavior data is used as a reference when determining whether to add the content to be calculated to the shared content list. The behavior data of each user reflects their interaction with the content in the system. These data can identify popular content types, styles or tags, and add popular content to the shared content list in a timely manner, avoiding simple "one-size-fits-all" recommendations that make the content type too single.

[0039] Furthermore, when determining whether to add the content to be calculated to the shared content list, the behavior data of each member of the team (such as likes, comments, shares, etc.) is also considered, so that the content in the shared content list conforms to the common interests of team members, making the content more popular, which is conducive to promoting interaction and cooperation within the team. By combining personal and team preference data, this application can more accurately predict and recommend content that members may be interested in. Compared with the traditional single recommendation mechanism, the recommendation mechanism that combines team and personal data can significantly improve the accuracy and content diversity of content recommendations, making team members more satisfied with the recommended content.

[0040] Figure 2 is a flow chart of the method for calculating the personal preference value in step S120. Figure 2 As shown, the method includes: Step S121, counting the personal behavior data of the type to be calculated in each dimensional feature; Step S122, counting the sum of all types of personal behavior data in each dimensional feature; Step S123, obtaining a personal feature preference value for each dimensional feature according to the ratio of the personal behavior data of the type of content to be calculated to the total personal behavior data of all types in each dimensional feature, wherein the personal feature preference value is used to indicate the popularity of the type of content in the individual user group; Step S124, calculating the geometric mean of the personal characteristic preference values ​​of all dimensional characteristics to obtain the personal preference value.

[0041] In step S121, the dimensional features refer to the various descriptive features of the content. Different content types under each dimensional feature will contain certain interaction data (such as clicks, shares, comments, etc.). These behavioral data reflect the degree of interest of individual users in the content type.

[0042] When the content to be calculated is a dance video or dance music, the dimensional features of the dance video or dance music are decomposed into one or more of the following: dance type features, dance content features, action type features, and rhythm type features. Among them, dance type features include: classical dance, square dance, folk dance, and popular dance; dance content features include: one or more of teaching content, performance content, competition content, and daily exercises. Action type features include: action difficulty features (zero basis, simple, moderate, slightly difficult...), action intensity features (low intensity, medium intensity, high intensity...), action part features (shoulder and neck, waist and abdomen, knees, whole body...); rhythm type features include: rhythm speed features (slow, medium, fast...), rhythm emotion features (soothing, brisk, high-pitched...).

[0043] For example, the content to be calculated is a dance video V, and the dance type feature of the video is: square dance; the action difficulty feature is: moderate; and the action intensity feature is: low intensity. Then, all individual user interaction data of the dance type "square dance" is counted; all individual user interaction data of the action difficulty feature "moderate" is counted; and all individual user interaction data of the action intensity feature "low intensity" is counted.

[0044] In step S122, the sum of all types of personal behavior data in each dimensional feature is calculated to provide a benchmark for subsequent calculations. As described above, taking the dance video V as an example, all personal user interaction data of all dance types are counted; all personal user interaction data of all action difficulty features are counted; and all personal user interaction data of all action intensity features are counted.

[0045] In step S123, the personal characteristic preference value is expressed according to the ratio of the personal behavior data of the type of content to be calculated in each dimension feature to the total personal behavior data of all types. Assuming that the user has watched the "square dance" video 300 times, and among all dance types (classical dance, square dance, folk dance, popular dance), the user has watched a total of 6000 times, then the personal characteristic preference value = 300 / 6000 = 0.05, then the personal characteristic preference value is 0.05.

[0046] In step S124, the personal preference value is calculated by means of the geometric mean according to the personal characteristic preference value of each dimensional feature. The geometric mean is used to comprehensively consider the influence of multiple dimensional features on personal preferences, and avoid the excessive influence of a single dimensional feature on the final preference value. The above method can achieve accurate modeling of user interests, provide strong support for content recommendation, and ensure that the recommended content meets the multi-dimensional preferences of individual users.

[0047] It can be seen from the above method that personal behavior data includes multiple behavior data, such as: number of plays, play duration, number of likes, number of collections, and number of shares, etc. For each type of behavior data, a personal behavior data characteristic preference value can be calculated.

[0048] According to an embodiment of the present application, the personal feature preference value of each dimensional feature is obtained according to the ratio of the personal behavior data of the type to which the content to be calculated belongs to in each dimensional feature to the sum of personal behavior data of all types, including: Obtain the preference value of the i-th personal behavior data feature in each dimensional feature according to the ratio of the i-th personal behavior data of the type to be calculated content in each dimensional feature to the sum of the i-th personal behavior data of all types, where i∈c, and the number of types of personal behavior data is c in total; The c types of personal behavior data feature preference values ​​are weighted and normalized to obtain the personal feature preference value of each dimension feature.

[0049] The above method refines the personal behavior data of the type of content to be calculated in each dimension feature, and calculates the preference value of each behavior data feature based on different types of behavior data (such as the number of plays, play duration, number of likes, etc.). Finally, the personal feature preference value of each dimension feature is calculated using weighted normalization. This method can capture the user's behavior preferences more carefully and further optimize content recommendations.

[0050] According to one embodiment of the present application, the personal preference value is calculated using the following formula:

[0051] Among them, score_p is the personal preference value, A k is the personal feature preference value of the k-th dimension feature. There are a total of z dimension features. sum(v i ) is the sum of the i-th type of personal behavior data of the type of content to be calculated, sum(v_all i ) is the sum of all types of individual behavior data of the ith type, α i is the weight of the i-th type of personal behavior data of the type to be calculated, and there are a total of c types of personal behavior data.

[0052] Figure 3 is a flow chart of the method for calculating the team preference value in step S130. Figure 3 As shown, the method includes: Step S131, counting the team behavior data of the type to which the content to be calculated belongs in each dimensional feature; Step S132, counting the total of all types of team behavior data in each dimensional feature; Step S133, obtaining a team feature preference value for each dimensional feature according to the ratio of the team behavior data of the type to be calculated content to the total team behavior data of all types in each dimensional feature, wherein the team feature preference value is used to indicate the popularity of the type to which the content belongs among team members; Step S134, calculating the geometric mean of the team feature preference values ​​of all dimensional features to obtain the team preference value.

[0053] This method and Figure 2 The method for calculating individual preference values ​​is similar to that in , the only difference is the statistical data. The specific steps of the method will not be repeated here. This method aims to calculate the team characteristic preference value of each dimension feature by statistically analyzing the interactive behavior data of team members, and finally obtain a comprehensive team preference value, which is used to better recommend content that meets the interests of the team and improve the participation and satisfaction of team members.

[0054] Furthermore, the team feature preference value of each dimensional feature is obtained according to the ratio of the team behavior data of the type to which the content to be calculated belongs in each dimensional feature to the total team behavior data of all types, including: Obtain the feature preference value of the first type of team behavior data in each dimensional feature according to the ratio of the first type of team behavior data of the type to be calculated in each dimensional feature to the sum of the first type of team behavior data of all types, where I∈C, and the number of types of team behavior data is C in total; The characteristic preference values ​​of C types of team behavior data are weighted and normalized to obtain the team characteristic preference value of each dimension feature.

[0055] According to one embodiment of the present application, the team preference value is calculated using the following formula:

[0056] Among them, score_g is the team preference value, B k is the team feature preference value of the kth dimension feature. There are a total of z dimension features. sum(v I ) is the sum of the first type of team behavior data of the type of content to be calculated, sum(v_all I ) is the sum of all types of the first type of team behavior data, α I is the weight value of the Ith type of team behavior data of the type to be calculated, and there are S types of team behavior data in total.

[0057] According to another embodiment of the present application, the team preference value is calculated based on the team behavior data of each dimensional feature and the behavior data of the team members on the shared content list.

[0058] In the above embodiment, the team behavior data refers to the behavior data (such as the number of plays, the number of favorites, the number of shares, etc.) generated by the team members with respect to the content in the shared content list, and the team members' behavior data on the shared content list refers to the behavior data generated by the team members with respect to the shared content list. The team members' behavior data on the shared content list includes: the team members' click data on the shared content list, the number of shares, and whether to delete one or more items in the shared content list. By analyzing the team members' behavior data on the shared content list, it is possible to understand the team members' interest and participation in the various types of content in the shared content list, and then infer the team's overall preference.

[0059] In the above embodiment, by collecting statistics and analyzing the interactive behavior data between team members and the shared content list, the interactive behavior of team members on the content and the interactive behavior of the team on the shared content list are comprehensively considered, and finally a team preference value that can reflect the overall preference of the team is obtained. This preference value can be used to determine whether the content is in line with their interests and improve the content recommendation effect.

[0060] According to another embodiment of the present application, the team preference value is calculated using the following formula:

[0061] Among them, score_g is the team preference value, B k is the team behavior data feature preference value of the kth dimension feature. There are a total of z dimension features. α I is the weight value of the I-th type of team behavior data, there are S types of team behavior data, X is the number of shared content lists, sum(v Ix ) is the I-th type of team behavior data of the type of content to be calculated in the x-th shared content list, sum(v_all Ix ) is the sum of the first type of team behavior data of all types in the xth shared content list, is a weighting coefficient of the shared content list x, and the weighting coefficient is calculated according to the behavior data of the team members on the shared content list.

[0062] It can be seen from the above formula that when a team has multiple shared content lists, multiple weighted coefficients are calculated according to the behavior data of each team member on each shared content list, and then the team preference value is calculated.

[0063] According to one embodiment of the present application, the weighting coefficient is calculated using the following formula:

[0064] When the shared content list is used normally, the weighting coefficient β is calculated using formula (1). When the shared content list is deleted, the weighting coefficient β is calculated using formula (2), where , , e and f are weight values, sum(E) is the total number of clicks on the shared content list, and sum(F) is the total number of shares on the shared content list.

[0065] According to an embodiment of the present application, the personal preference value is calculated based on the personal behavior data of each dimension feature within multiple specified time periods; and / or The team preference value is calculated based on the team behavior data of each dimension feature in multiple specified time periods and the behavior data of team members on the shared content list.

[0066] In the above embodiment, by dividing the user behavior data of individuals and teams into multiple time periods in chronological order from far to near, the interest changes of the preference values ​​of individuals and / or teams in different time periods are obtained, which can more accurately capture the preference trends of users and teams, so as to make more personalized content recommendations.

[0067] According to one embodiment of the present application, the preference matching value is calculated using the following formula:

[0068] Among them, S is the preference matching value, μ1 is the weight value of the individual preference value, μ2 is the weight value of the team preference value, δ ti is the weight value of the personal preference value in time period ti and and , No. Time period The time period is closer to the current time. There are m time periods in total. is the weight value of the team preference value in time period tj and and , No. Time period The time period is closer to the current time. There are n time periods in total. score_p ti is the personal preference value for time period ti, score_g tj is the team preference value in time period tj.

[0069] The above describes the implementation method and advantages of the embodiments of the present application through multiple embodiments. The following describes the specific processing process of the embodiments of the present application in detail with the example of a dance video.

[0070] Using the above method, dance videos are selected from the dance video database of the server and added to the shared video list of dance team A (hereinafter referred to as dance team A) to generate a dance list. There are 10 dancers in dance team A. The historical data of the members of the dance team are summarized in Table 1: (Note: The time division is divided into the past 3 years, the past six months, and the past month from the farthest to the nearest. The past 3 years do not include the past six months, and the past six months do not include the past January.) Table 1

[0071] As shown in Table 1, the individual users in the above system are users who have joined dance team A among all registered users in the system. The dimensional features of dance videos in the content database are decomposed into dance category features and action difficulty features. The user's interactive behavior data on the content includes: the number of plays and the number of collections, and the user's behavior data on the dance list includes the number of clicks and the number of shares.

[0072] Combined with Table 1, it can be seen that in the formula of this method, the parameters and assignment weights are shown in Table 2 below: Table 2

[0073] First, a dance video D is randomly selected from the content database. The dance category feature of the video is square dance, and the action difficulty feature is moderate. Then the data in Table 1-2 is combined with the above formula for calculation.

[0074] (1) Personal characteristic preference value: Personal characteristic preference values ​​of square dancing in the past three years A1-1:

[0076] The personal characteristic preference value of moderate action difficulty in the past three years A2-1:

[0078] The personal preference values ​​of dance videos D in the past three years are:

[0079] Similarly, the personal characteristic preference value of square dancing in the past six months is A2-1=0.59; The personal characteristic preference value of moderate action difficulty in the past six months is A2-2=0.68; The personal preference value of dance video D in the past six months is: score_p2=0.63.

[0080] Similarly, the personal characteristic preference value of square dancing in the past month is A3-1=0.58; The personal characteristic preference value of moderate action difficulty in the past month is A3-2=0.81; The personal preference value of dance video D in the past month is: score_p3=0.69.

[0081] (2) Team characteristic preference value From the data in Table 1 and Table 2, we can see that the weighted coefficients β of the two playlists of dance team A are as shown in Table 3: Table 3

[0082] Combining Table 1-3 and the above formula, we can calculate: The team characteristic preference values ​​of square dance in the past three years are B1-1:

[0083] The team characteristic preference value B2-1 with moderate action difficulty in the past three years:

[0084] The team preference values ​​of dance video D in the past three years are:

[0085] Similarly, the team characteristic preference value of square dancing in the past six months is B2-1=0.79; The characteristic preference value of the team with moderate action difficulty in the past six months is B2-2=0.61; The team preference value of dance video D in the past six months is: score_ g2 =0.69.

[0086] Similarly, the team characteristic preference value of square dancing in the past month is B3-1=0.74; The team characteristic preference value for moderate action difficulty in the past month is B3-2=0.22; The team preference value of dance video D in the past month is: score_ g3 =0.40.

[0087] (3) Preference matching value

[0088] The preference matching value of dance video D is 0.55. Assuming that the preference matching threshold is 0.4, dance video D is added to the shared video list until the number of dance videos in the shared video list reaches a preset number threshold (for example, 20), and the calculation stops.

[0089] The shared video list is displayed on the client of the dance team members. For example, the shared dance video list entrance is displayed in a specific area of ​​the dance team space page. Clicking the entrance can enter the list details, which include but are not limited to the video cover, video title, publisher nickname and other information in order.

[0090] In addition, dance team members can operate and learn the shared dance video list online on the client. For example, dance team members (including team leader) can operate the video list online on the client function page, such as clicking on the video to watch, removing disliked content, adding other favorite content, and adjusting the order of the videos. When adding other favorite content, users can be supported to locate the required content by searching, or they can be provided with a pop-up window of alternative content so that users can select the required content from the pop-up window.

[0091] Furthermore, the dance team leader can lock or unlock the list adjusted by the dance team user members on the client function page. After locking, the dance team users cannot change it, and after unlocking, they can change it again. The dance team leader can refresh the current shared dance video list on the client function page, and then re-execute the above method to update the videos in the shared dance video list.

[0092] Finally, a shared dance music list is generated based on the locked dance video list. Figure 4 The interface diagram of the shared dance video list and shared dance music list is shown. Among them, the dance music in the shared dance music list corresponds one-to-one to the dance videos in the shared content list. When saving a dance music list, the shared dance music list name is randomly generated and renaming is supported. Among them, the shared video list is used for online learning of dance team users, and the shared dance music list is used for offline speaker playback of the dance team. The videos in the dance video list provide dance team users with content that is recognized by all members, which is convenient for dance team members to preview the content to be learned online, and at the same time provide convenience for the dance team leader to select online content, offline teaching, and offline dance with the dance music list.

[0093] Figure 5 The present invention is a device for generating a shared content list for a team according to an embodiment of the present application. The device 100 includes: The extraction module 110 is used to randomly extract a content to be calculated from the content database and obtain a content tag of the content to be calculated, where the content tag is used to describe multiple dimensional features of the content to be calculated; A first calculation module 120 is used to calculate a personal preference value based on personal behavior data of each dimension feature, wherein the personal behavior data is used to represent: interaction behavior data between a personal user in the system and content in a content database, and the personal preference value is used to represent the popularity of the content in a personal user group; A second calculation module 130 is used to calculate a team preference value based on the team behavior data of each dimension feature, wherein the team behavior data is used to represent: interaction behavior data of all members in the team with the content in the shared content list, and the team preference value is used to represent the popularity of the content among the team members; The third calculation module 140 is used to calculate the preference matching value of the content to be calculated according to the personal preference value and the team preference value and the corresponding weight value, wherein the preference matching value is used to indicate the popularity of the content among the individual user and the team member; The judgment module 150 is configured to add the content to be calculated to the shared content list when the preference matching value is greater than a preference matching threshold.

[0094] In another aspect, the present application also provides a computing device, see Figure 6 , Figure 6 1 is a block diagram of the structural principle of a computing device according to an embodiment of the present application. Figure 6 As shown, the computing device includes a processor 601 and a memory 602 storing computer program instructions; when the processor 601 executes the computer program instructions, the method for generating a shared content list for a team in the aforementioned embodiment is implemented.

[0095] Specifically, the processor 601 may include a central processing unit (CPU) or a graphics processing unit (GPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application. The memory 602 may include a memory for data or instructions. For example, the memory 602 may be at least one of the following: a hard disk drive (HDD), a read-only memory (ROM), a random access memory (RAM), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, a universal serial bus (USB) drive or other physical / tangible memory storage device. For another example, the memory 602 includes a removable or non-removable (or fixed) medium. For another example, the memory 602 may be inside or outside the integrated gateway disaster recovery device. The memory 602 may be a non-volatile solid-state memory. In other words, typically the memory 602 includes a tangible (non-transitory) computer-readable storage medium (such as a memory device) encoded with executable instructions, wherein when the stored executable instructions are executed by the processor 601 (such as executed by one or more processors), the method for generating a shared content list for a team in an embodiment of the present application can be implemented.

[0096] In one example, Figure 6The electronic device shown may also include a communication interface 603 and a bus 610. The processor 601, the memory 602, and the communication interface 603 are connected and communicate with each other via the bus 610. The communication interface 603 is mainly used to implement communication between modules, devices, units, and / or devices in the electronic device.

[0097] The bus 610 includes hardware, software or both, and can couple the components of the online data traffic billing device to each other. For example, the bus may include at least one of the following: an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-side bus (FSB), a hypertransport (HT) interconnect, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus or other suitable bus. The bus 610 may include one or more buses. Although the embodiments of the present application describe or show a specific bus, the embodiments of the present application may consider any suitable bus or interconnection method.

[0098] On the other hand, an embodiment of the present application further provides a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the aforementioned method for generating a shared content list for a team is implemented.

[0099] The above exemplarily describes the flowchart and / or block diagram of the method and system of the embodiment of the present application, and describes the relevant various aspects. It should be understood that each box or combination thereof in the flowchart and / or block diagram can be implemented by computer program instructions, or can be implemented by special hardware that performs a specified function or action, or can be implemented by a combination of special hardware and computer instructions. For example, these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to form a machine that enables these instructions executed by such a processor to enable the implementation of the functions / actions specified in each box or combination thereof in the flowchart and / or block diagram. Such a processor can be a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit.

[0100] The functional blocks shown in the structural block diagram of the embodiment of the present application can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc.; when implemented in software, it is a program or code segment used to perform the required task. The program or code segment can be stored in a memory, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0101] The above embodiments are only used to illustrate the present application, and are not intended to limit the present application. Ordinary technicians in the relevant technical field may make various changes and modifications without departing from the scope of the present application. Therefore, all equivalent technical solutions should also fall within the scope disclosed in the present application.

Claims

1. A method for generating a shared content list for a team, characterized in that: include: Randomly extract a content to be calculated from the content database, and obtain a content tag of the content to be calculated, where the content tag is used to describe multiple dimensional features of the content to be calculated; A personal preference value is calculated based on personal behavior data of each dimensional feature, wherein the personal behavior data is used to represent: interaction behavior data between individual users in the system and content in the content database, and the personal preference value is used to represent the popularity of the dimensional feature to which the content belongs in the individual user group; A team preference value is calculated based on the team behavior data of each dimensional feature, wherein the team behavior data is used to represent: interaction behavior data of all members in the team with the content in the shared content list, and the team preference value is used to represent the popularity of the dimensional feature to which the content belongs among team members; The preference matching value of the content to be calculated is calculated according to the individual preference value, the team preference value and the corresponding weight value, and the preference matching value is used to indicate the popularity of the content among individual users and team members; When the preference matching value is greater than the preference matching threshold, the content to be calculated is added to the shared content list.

2. The method according to claim 1, characterized in that The personal preference values ​​calculated based on the personal behavior data of each dimension feature include: Count the personal behavior data of the type of content to be calculated in each dimension feature; Count the sum of all types of personal behavior data in each dimension feature; Obtain a personal feature preference value for each dimensional feature according to the ratio of the personal behavior data of the type of content to be calculated in each dimensional feature to the sum of personal behavior data of all types, wherein the personal feature preference value is used to indicate the popularity of the type of content in the individual user group; The personal preference value is obtained by calculating the geometric mean of the personal characteristic preference values ​​of all dimensional features.

3. The method according to claim 2, characterized in that The personal characteristic preference value of each dimensional feature is obtained based on the ratio of the personal behavior data of the type of content to be calculated in each dimensional feature to the sum of all types of personal behavior data, including: Obtain the preference value of the i-th personal behavior data feature in each dimensional feature according to the ratio of the i-th personal behavior data of the type to be calculated content in each dimensional feature to the sum of the i-th personal behavior data of all types, where i∈c, and the number of types of personal behavior data is c in total; The c types of personal behavior data feature preference values ​​are weighted and normalized to obtain the personal feature preference value of each dimension feature.

4. The method according to claim 1, characterized in that: The team preference values ​​calculated based on the team behavior data of each dimension feature include: Count the team behavior data of the type of content to be calculated in each dimension feature; Count the total of all types of team behavior data in each dimension feature; Obtain a team feature preference value for each dimensional feature according to the ratio of the team behavior data of the type of content to be calculated in each dimensional feature to the total team behavior data of all types, wherein the team feature preference value is used to indicate the popularity of the type of content among team members; The team preference value is obtained by calculating the geometric mean of the team characteristic preference values ​​of all dimensional characteristics.

5. The method according to claim 4, characterized in that The team feature preference values ​​of each dimensional feature are obtained based on the ratio of the team behavior data of the type of content to be calculated in each dimensional feature to the total team behavior data of all types, including: Obtain the feature preference value of the first type of team behavior data in each dimensional feature according to the ratio of the first type of team behavior data of the type to be calculated in each dimensional feature to the sum of the first type of team behavior data of all types, where I∈C, and the number of types of team behavior data is C in total; The characteristic preference values ​​of C types of team behavior data are weighted and normalized to obtain the team characteristic preference value of each dimension feature.

6. The method according to claim 1, characterized in that The team preference value is calculated based on the team behavior data of each dimension feature and the behavior data of team members on the shared content list.

7. The method according to claim 6, characterized in that The team preference value is calculated using the following formula: Among them, score_g is the team preference value, B k is the team behavior data feature preference value of the kth dimension feature. There are a total of z dimension features. α I is the weight value of the I-th type of team behavior data, there are s types of team behavior data, X is the number of shared content lists, sum(v Ix ) is the I-th type of team behavior data of the type of content to be calculated in the x-th shared content list, sum(v_all Ix ) is the sum of the first type of team behavior data of all types in the xth shared content list, is a weighting coefficient of the shared content list x, and the weighting coefficient is calculated according to the behavior data of the team members on the shared content list.

8. The method according to claim 6, characterized in that The behavior data of the team members on the shared content list includes: the click data of the team members on the shared content list, the number of shares, and whether to delete one or more items in the shared content list.

9. The method according to claim 1, characterized in that: Calculate the personal preference value based on the personal behavior data of each dimension feature within multiple specified time periods; and / or The team preference value is calculated based on the team behavior data of each dimension feature in multiple specified time periods and the behavior data of team members on the shared content list.

10. The method according to claim 1, characterized in that The individual users in the system include: all users registered in the system, users who have joined a team among all users registered in the system, and any one of users who have joined a target team among all users registered in the system.

11. The method according to any one of claims 1 to 10, characterized in that: The content includes one or more of a dance video and dance music, wherein the dance music is a song separated from the dance video.

12. The method according to claim 1, characterized in that When the content to be calculated is a dance video or dance music, the dimensional features of the dance video or dance music are decomposed into one or more of dance type features, action type features and rhythm type features.

13. A device for generating a shared content list for a team, characterized in that: include: An extraction module, used to randomly extract a content to be calculated from the content database and obtain a content tag of the content to be calculated, wherein the content tag is used to describe multiple dimensional features of the content to be calculated; A first calculation module is used to calculate a personal preference value based on personal behavior data of each dimension feature, wherein the personal behavior data is used to represent: interaction behavior data between a personal user in the system and content in a content database, and the personal preference value is used to represent the popularity of the content in a personal user group; A second calculation module is used to calculate a team preference value based on the team behavior data of each dimension feature, wherein the team behavior data is used to represent: interaction behavior data of all members in the team with the content in the shared content list, and the team preference value is used to represent the popularity of the content among the team members; A third calculation module is used to calculate the preference matching value of the content to be calculated according to the personal preference value and the team preference value and the corresponding weight value, wherein the preference matching value is used to indicate the popularity of the content among individual users and team members; The judgment module is configured to add the content to be calculated to the shared content list when the preference matching value is greater than a preference matching threshold.

14. A computing device, characterized in that include: processor; A memory storing a computer program, which, when executed by a processor, implements the method for generating a shared content list for a team according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for generating a shared content list for a team according to any one of claims 1 to 12 is implemented.

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