CA3150500C - Uploader matching method and device - Google Patents

Uploader matching method and device Download PDF

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CA3150500C
CA3150500C CA3150500A CA3150500A CA3150500C CA 3150500 C CA3150500 C CA 3150500C CA 3150500 A CA3150500 A CA 3150500A CA 3150500 A CA3150500 A CA 3150500A CA 3150500 C CA3150500 C CA 3150500C
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CA3150500A1 (en
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Liangwu XU
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10353744 Canada Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/7867—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings

Abstract

An uploader rnatching method and device, which belong to the field of computer information technologies and are applicable to the field of short videos. The method comprises: acquiring released video data of uploaders, determining, according to the released video data, comprehensive score values of the uploaders from one or more dimensional feature scores and preset weights of the dimensional features, and filtering a target uploader according to the comprehensive score values of the uploaders (101); counting released video data of the target uploader according to a preset tag rule, and generating corresponding uploader word vectors of one or more video category tags (102); acquiring video data played back by a user within a first preset period, counting the video data played back by the user according to the preset tag rule, and generating corresponding user word vectors of the one or more video category tags (103); performing corresponding matching between the corresponding video category tags of the uploader word vectors and the user word vectors, to acquire an uploader word vector result that reaches a target degree of matching with the user, and determining corresponding uploader information according to the uploader word vector result (104).

Description

UPLOADER MATCHING METHOD AND DEVICE
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of computer information technology, and more particularly to an uploader matching method and an uploader matching device.
Description of Related Art
[0002] Faced with massive amounts of video resources and users numbering in the hundred millions in the field of short video recommendation, it is of crucial importance as how to recommend high-quality videos preferred by users to target users, so as to solve the problem of information overload and to enhance time duration of stay and satisfaction of users. Qualified uploaders (persons who upload video and audio files onto video websites, forums, and ftp sites) are high-quality video releasers proven by many users, the recommendation of qualified uploaders to users with similar user portraits enables the users to more conveniently acquire quality videos of interest to them, and such recommendation will increase user adhesion and satisfaction to a greater extent. How to qualitatively and quantitatively evaluate the qualities of short video uploaders directly decides whether most similar and high-quality uploaders can be recommended precisely to target users.
[0003] Currently, the number of users registered in some video platforms reaches hundred millions, with daily UV accesses exceeding ten millions, and the volume of plays per day is even higher in mobile ends. In order that users find out contents of interest to them from massive videos, precise user portraits and the recommending system exert significant functions. Once qualified uploaders are recommended to and followed by most similar target users, it will be made possible for the users to consistently watch high-quality videos of possible interest to them.

SUMMARY OF THE INVENTION
[0004] In order to overcome problems pending in the state of the art, embodiments of the present invention provide an uploader matching method and an uploader matching device, whereby comprehensive evaluation of uploaders is achieved through a comprehensive scoring scheme with multiple data sources and multi-dimensional variables, high-quality uploader information is extracted therefrom, and matched with user word vector that is realized through user portrait, and high-quality uploaders are finally recommended to target users with high matching degrees, so that CTR (Click-Through-Rate), user playback volume and average playback integrity are enhanced, and user experience is enhanced.
[0005] The technical solutions are as follows.
[0006] According to one aspect, there is provided an uploader matching method that comprises:
[0007] obtaining released video data of uploaders, determining comprehensive score values of the uploaders from one or more dimension feature score(s) according to the released video data, and screening out target uploaders according to the comprehensive score values of the uploaders;
[0008] making statistics on the released video data of the target uploaders according to a preset tagging rule, and generating corresponding uploader word vector of one or more video category tag(s);
[0009] obtaining user played-back video data within a first preset period, making statistics on the user played-back video data according to the preset tagging rule, and generating corresponding user word vector of the one or more video category tag(s); and
[0010] correspondingly matching the corresponding video category tags of the uploader word vector and the user word vector, obtaining an uploader word vector result that has reached a target matching degree with the user, and determining corresponding uploader information according to the uploader word vector result.
[0011] Further, the step of determining comprehensive score values of the uploaders from one or more dimension feature score(s) according to the released video data, and screening out target uploaders according to the comprehensive score values of the uploaders includes:
[0012] calculating score(s) of one or more dimension feature(s) in released video activity scores of the uploaders, video quality scores of the uploaders, and video verticality scores of the uploaders according to the released video data;
[0013] calculating the comprehensive score values of the uploaders according to the one or more dimension feature score(s); and
[0014] selecting the uploaders who rank top N to serve as the target uploaders according to a sequence of the comprehensive score values of the uploaders arranged in a decreasing order, wherein N is an integer greater than 1.
[0015] Further, the steps of calculating score(s) of one or more dimension feature(s) in released video activity scores of the uploaders, video quality scores of the uploaders, and video verticality scores of the uploaders according to the released video data;
calculating the comprehensive score values of the uploaders according to the one or more dimension feature score(s) include:
[0016] sorting the number of released videos of the uploaders within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with time decay, mapping the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], determining respective weight indices of the number of released videos and the volume of released videos played back, wherein a first weight index xl and a second weight index x2 are each evaluated as a decimal between 0 and 1, thereafter multiplying the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back, and calculating to obtain the released video activity scores of the uploaders;
[0017] sorting the number of sharings, the number of praisings, the number of commentings, a proportion of positive comments, the number of listings as favorites, the number of followings and released video playback integrity rates of released videos of the uploaders within the second preset period respectively combined with time decay, mapping the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively determining respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1, thereafter summating and averaging the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings, subsequently multiplying the summating and averaging result with the respective weight indices of the released video playback integrity rates, and calculating to obtain the video quality scores of the uploaders;
[0018] sorting category proportions of released videos of the uploaders within the second preset period in combination with time decay, mapping the sorted category proportions to a range of [x10,1], determining respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1, thereafter multiplying the respective weight indices of the category proportions, and calculating to obtain the video verticality scores of the uploaders; and
[0019] multiplying the released video activity scores, the video quality scores and the video verticality scores, and calculating to obtain the comprehensive score values of the uploaders.
[0020] Moreover, the method further comprises: a step of obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10:
[0021] respectively taking various dimension feature scores to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables, taking following degrees of the uploaders after exposure as dependent variables, and employing a RandomForest algorithm and a GBDT
algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
[0022] Further, the step of obtaining user played-back video data within a first preset period, making statistics on the user played-back video data according to the preset tagging rule, and generating corresponding user word vector of the one or more video category tag(s) includes:
[0023] eliminating hotspot videos and miss-clicked videos from the user played-back video data, counting out top N target user tags each of whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule, calculating the target user tags, and generating corresponding user word vectors of the target user tags, wherein N is an integer greater than 1.
[0024] Moreover, the method further comprises:
[0025] recommending the uploader information to the user; and/or
[0026] pushing a video of the video category tag corresponding to the uploader word vector result to the user.
[0027] According to another aspect, there is provided an uploader matching device that comprises:
[0028] a calculating module, for obtaining released video data of uploaders, determining comprehensive score values of the uploaders from one or more dimension feature score(s) according to the released video data, and screening out target uploaders according to the comprehensive score values of the uploaders;
[0029] an uploader word vector generating module, for making statistics on the released video data of the target uploaders according to a preset tagging rule, and generating corresponding uploader word vector of one or more video category tag(s);
[0030] a user word vector generating module, for obtaining user played-back video data within a first preset period, making statistics on the user played-back video data according to the preset tagging rule, and generating corresponding user word vector of the one or more video category tag(s); and
[0031] a matching module, for correspondingly matching the corresponding video category tags of the uploader word vector and the user word vector, obtaining an uploader word vector result that has reached a target matching degree with the user, and determining corresponding uploader information according to the uploader word vector result.
[0032] Further, the calculating module includes a first calculating sub-module, a second calculating sub-module and a screening sub-module, of which:
[0033] the first calculating sub-module is employed for calculating score(s) of one or more dimension feature(s) in released video activity scores of the uploaders, video quality scores of the uploaders, and video verticality scores of' the uploaders according to the released video data;
[0034] the second calculating sub-module is employed for calculating the comprehensive score values of the uploaders according to the one or more dimension feature score(s); and
[0035] the screening sub-module is employed for selecting the uploaders who rank top N to serve as the target uploaders according to a sequence of the comprehensive score values of the uploaders arranged in a decreasing order, wherein N is an integer greater than 1.
[0036] Further, the first calculating sub-module is employed for:
[0037] sorting the number of released videos of the uploaders within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with time decay, mapping the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], determining respective weight indices of the number of released videos and the volume of released videos played back, wherein a first weight index xl and a second weight index x2 are each evaluated as a decimal between 0 and 1, thereafter multiplying the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back, and calculating to obtain the released video activity scores of the uploaders;
[0038] sorting the number of sharings, the number of praisings, the number of commentings, a proportion of positive comments, the number of listings as favorites, the number of followings and released video playback integrity rates of released videos of the uploaders within the second preset period respectively combined with time decay, mapping the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively determining respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1, thereafter summating and averaging the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings, subsequently multiplying the summating and averaging result with the respective weight indices of the released video playback integrity rates, and calculating to obtain the video quality scores of the uploaders;
and
[0039] sorting category proportions of released videos of the uploaders within the second preset period in combination with time decay, mapping the sorted category proportions to a range of [x10,1], determining respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1, thereafter multiplying the respective weight indices of the category proportions, and calculating to obtain the video verticality scores of the uploaders; and
[0040] the second calculating sub-module is employed for:
[0041] multiplying the released video activity scores, the video quality scores and the video verticality scores, and calculating to obtain the comprehensive score values of the uploaders.
[0042] Further, a step of obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 includes:
[0043] respectively taking various dimension feature scores to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables, taking following degrees of the uploaders after exposure as dependent variables, and employing a RandomForest algorithm and a GBDT
algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
[0044] Further, obtaining user played-back video data within a first preset period, making statistics on the user played-back video data according to the preset tagging rule, and generating corresponding user word vector of the one or more video category tag(s) includes:
[0045] eliminating hotspot videos and miss-clicked videos from the user played-back video data, counting out top N target user tags each of whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule, calculating the target user tags, and generating corresponding user word vectors of the target user tags, wherein N is an integer greater than 1.
[0046] Further, the device further comprises a data recommending module for recommending the uploader information to the user; and/or pushing a video of the video category tag corresponding to the uploader word vector result to the user.
[0047] The technical solutions provided by the embodiments of the present invention bring about the following advantageous effects.
[0048] 1. By sorting out in overall such data as released video information of uploaders and user behaviors, history records of user playbacks and such multi-dimensional information as relevant to the listing as favorites, sharing, praising, commenting and playback integrities of videos released by uploaders are obtained, a comprehensive scoring scheme based on multiple data sources and multi-dimensional variables achieves comprehensive evaluation of uploaders, and high-quality uploader information is extracted therefrom.
[0049] 2. Various dimension weights for evaluating uploader qualities are obtained through model training by corresponding algorithms, and evaluation of uploader qualities is made more precise.
[0050] 3. Uploaders are finely classified according to released video category tags, whereby precision of uploader portraits is enhanced.
[0051] 4. During the process of drawing portraits of users, hotspot videos and possibly miss-clicked videos are eliminated, portraits are drawn for the users by differentiating different interest tags, result sets are proportionally formed, and precision in recommending result sets is enhanced.
[0052] 5. Time decay is taken into consideration in the processes of calculating user vector and calculating uploader vector, whereby transfer of interest points is enhanced.
[0053] 6. By matching the high-quality uploader word vector with the user word vector of a precise user portrait, high-quality uploaders are finally recommended to target users with high matching degree, by following uploaders similar to themselves, users are facilitated to timely watch high-quality videos, whereby satisfaction of the users is enhanced, comparison report indicators are obtained through AB test, whereby CTR, user playback volume and average playback integrity are enhanced, and user experience is enhanced as a whole.
BRIEF DESCRIPTION OF THE DRAWINGS
[0054] To more clearly describe the technical solutions in the embodiments of the present invention, drawings required to illustrate the embodiments are briefly introduced below.
Apparently, the drawings introduced below are merely directed to some embodiments of the present invention, while persons ordinarily skilled in the art may further acquire other drawings on the basis of these drawings without spending creative effort in the process.
[0055] Fig. 1 is a flowchart illustrating the uploader matching method provided by the embodiments of the present invention;
[0056] Fig. 2 is a flowchart illustrating sub-steps of step 101 in Fig. 1;
[0057] Fig. 3 illustrates a preferred mode of execution for the setup of dimension features of the uploader comprehensive score values and score calculation thereof;
[0058] Fig. 4 illustrates a preferred mode of execution for the generation of the uploader word vector and the user word vector, and the corresponding tags matching; and
[0059] Fig. 5 is a view schematically illustrating the structure of the uploader matching device provided by the embodiments of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0060] To make more lucid and clear the objectives, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and comprehensively described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the embodiments as described are merely partial, rather than the entire, embodiments of the present invention.
Any other embodiments makeable by persons ordinarily skilled in the art on the basis of the embodiments in the present invention without creative effort shall all fall within the protection scope of the present invention.
[0061] As should be noted, the terms "first" and "second" etc. are merely used for the purpose of description, and should not be understood to indicate or implicate relative importance or imply the number of technical features expressed thereby. Therefore, a feature defined by "first" or "second" can explicitly or implicitly include one or more such feature(s).
The wording "a plurality of/plural" as used in the description of the present invention means "two or more" unless specifically defined otherwise.
[0062] In the uploader matching method and device provided by the embodiments of the present invention, by sorting out in overall such data as released video information of uploaders and user behaviors, history records of user playbacks and such multi-dimensional information as relevant to the listing as favorites, sharing, praising, commenting and playback integrities of videos released by uploaders are obtained, a comprehensive scoring scheme based on multiple data sources and multi-dimensional variables achieves comprehensive evaluation of uploaders, and high-quality uploader information is extracted therefrom; moreover, matching is performed with user word vector realized through user portrait, and high-quality uploaders are finally recommended to target users with high matching degrees, so that CTR (Click-Through-Rate), user playback volume and average playback integrity are enhanced, and user experience is enhanced.
Accordingly, the uploader matching method and device are applicable to such application scenarios as short video data processing, data matching or data pushing in the field of short video platforms.
[0063] The uploader matching method and device provided by the embodiments of the present invention are described in greater detail below in conjunction with specific embodiments and accompanying drawings.
[0064] Fig. 1 is a flowchart illustrating the uploader matching method provided by the embodiments of the present invention. Fig. 2 is a flowchart illustrating sub-steps of step 101 in Fig. 1. Fig. 3 is a flowchart illustrating steps secondary to the sub-step 1012 in Fig.
2.
[0065] As shown in Fig. 1, the uploader matching method provided by the embodiments of the present invention mainly comprises steps 101, 102, 103 and 104.
[0066] 101 - obtaining released video data of uploaders, determining comprehensive score values of the uploaders from one or more dimension feature score(s) according to the released video data, and screening out target uploaders according to the comprehensive score values of the uploaders.
[0067] Specifically, as shown in Fig. 2, step 101 may include the following sub-steps:
[0068] 1011 ¨ obtaining released video data of uploaders;
[0069] 1012 ¨ calculating score(s) of one or more dimension feature(s) in released video activity scores of the uploaders, video quality scores of the uploaders, and video verticality scores of the uploaders according to the released video data;
[0070] 1013 ¨ calculating the comprehensive score values of the uploaders according to the one or more dimension feature score(s).
[0071] Specifically, the released video activity scores, the video quality scores, and the video verticality scores are multiplied to calculate and obtain the comprehensive score values of the uploaders.
[0072] 1014 - selecting the uploaders who rank top N to serve as the target uploaders according to a sequence of the comprehensive score values of the uploaders arranged in a decreasing order, wherein N is an integer greater than 1.
[0073] Specifically, sub-step 1012 further includes the following secondary steps:
[0074] 1012a ¨ sorting the number of released videos of the uploaders within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with time decay, mapping the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], determining respective weight indices of the number of released videos and the volume of released videos played back, wherein a first weight index xi and a second weight index x2 are each evaluated as a decimal between 0 and 1, thereafter multiplying the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back, and calculating to obtain the released video activity scores of the uploaders;
[0075] 1012b ¨ sorting the number of sharings, the number of praisings, the number of commentings, a proportion of positive comments, the number of listings as favorites, the number of followings and released video playback integrity rates of released videos of the uploaders within the second preset period respectively combined with time decay, mapping the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively determining respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1, thereafter summating and averaging the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings, subsequently multiplying the summating and averaging result with the respective weight indices of the released video playback integrity rates, and calculating to obtain the video quality scores of the uploaders; and
[0076] 1012c - sorting category proportions of released videos of the uploaders within the second preset period in combination with time decay, mapping the sorted category proportions to a range of [x10,1], determining respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1, thereafter multiplying the respective weight indices of the category proportions, and calculating to obtain the video verticality scores of the uploaders.
[0077] The step of obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 is as follows:
[0078] respectively taking various dimension feature scores to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables, taking following degrees of the uploaders after exposure as dependent variables, and employing a RandomForest algorithm and a GBDT
algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10. The exposed following degrees of the uploaders are aimed to recommend the uploaders to target users, whether the users follow after exposure is specifically to take being followed as a positive sample of 1, and to take not being followed as a negative sample of 0, considering the problem of balance between positive and negative samples, the proportion between positive and negative samples is usually selected as 1:1 or 1:2.
[0079] It is possible to employ the RandomForest algorithm and the GBDT
algorithm to construct a weight calculation model prior to the step of obtaining preset weights of the various dimension features, of course, in the case of not departing from the inventive conception of the present invention, it is also possible to base on requirements to employ any other possible weight calculation mode or calculation model in the prior art, to which no particular restriction is made in the embodiments of the present invention.
[0080] Fig. 3 illustrates a preferred mode of execution for the setup of dimension features of the uploader comprehensive score values and score calculation thereof As shown in Fig. 3, the three big dimension indicators of activity of the uploaders, quality of the uploaders, and verticality of the uploaders are mainly considered in this preferred mode.
[0081] The activities of the uploaders are one of the indicators, in which the numbers of released videos (combined with time decay) within the recent three months are employed for sorting, the sorting is mapped to a range of [x1,1], and it is avoided that some few uploaders differ from the majority in the order of magnitude, so as to render the dimension meaningless, wherein xl is a decimal between 0 and 1, and can be obtained through model learning as a weight parameter. Another indicator of activity is the volumes of videos played backed within the recent three months sorted according to time decay, and mapped to [x2,1], the aforementioned two indicators are multiplied to obtain scores of the dimension of activity of the uploaders. The quality scores of the uploaders are calculated by averaging the scores of such dimensions as sharing, praising, commenting, the proportion of positive comments, listing as favorites and following, and the averaging result is then multiplied with released video playback integrity rates to obtain the scores of the quality dimension. The verticality scores of the uploaders are obtained by calculating verticalities of category proportions of released videos within the recent three months. Finally, the three dimensions of the uploaders in terms of activity, quality and verticality are multiplied to obtain the final comprehensive score values of the uploaders.
[0082] As should be noted, the evaluation of the above sorting N and the specific time period selection for the second preset period can be correspondingly set according to requirements, for instance, N can be set as 3 and the second preset period can be set as three months, to which no particular restriction is made in the embodiments of the present invention.
[0083] As is notable, the process of step 101 can as well be realized by modes other than the mode recited in the aforementioned step, and these specific modes are not restricted in the embodiments of the present invention.
[0084] 102 - making statistics on the released video data of the target uploaders according to a preset tagging rule, and generating corresponding uploader word vector of one or more video category tag(s).
[0085] The specific categories and number of the video category tags can be correspondingly set according to requirements, for instance, the video category can include athletics, finance and economics, and amusement, etc., to which no particular restriction is made in the embodiments of the present invention.
[0086] 103 - obtaining user played-back video data within a first preset period, making statistics on the user played-back video data according to the preset tagging rule, and generating corresponding user word vector of the one or more video category tag(s).
[0087] Specifically, hotspot videos and miss-clicked videos are eliminated from the user played-back video data, top N target user tags each of whose number of videos occupies a proportion that is not lower than a preset proportion are counted out according to the preset tagging rule, the target user tags are calculated, and corresponding user word vectors of the target user tags are generated, wherein N is an integer greater than 1.
[0088] As is notable, the process of step 103 can as well be realized by modes other than the mode recited in the aforementioned step, and these specific modes are not restricted in the embodiments of the present invention.
[0089] 104 - correspondingly matching the corresponding video category tags of the uploader word vector and the user word vector, obtaining an uploader word vector result that has reached a target matching degree with the user, and determining corresponding uploader information according to the uploader word vector result.
[0090] Fig. 4 illustrates a preferred mode of execution for the generation of the uploader word vector and the user word vector, and the corresponding tags matching. As shown in Fig.
4, in this preferred mode, the uploaders employ the word vector representation mode calculated and obtained according to videos released in the recent three months in combination with time delay. Vector representations of users are obtained according to playback history in the recent one month, top 3 with each category occupying a proportion greater than 10% are counted in accordance with video classification tags of watching history, it is found out through statistical analysis that these are indeed main interest points of the users, while other videos in the watching history are usually some hotspot videos or miss-clicked cases that should be eliminated, so as to ensure precision of user portraits.
[0091] As is notable, the process of step 104 can as well be realized by modes other than the mode recited in the aforementioned step, and these specific modes are not restricted in the embodiments of the present invention.
[0092] In addition, preferably, besides comprising the aforementioned steps 101, 102, 103 and 104, the uploader matching method provided by the embodiments of the present invention further comprises the following step:
[0093] recommending the uploader information to the user; and/or pushing a video of the video category tag corresponding to the uploader word vector result to the user.
Specifically, top ranking Ns are selected through similarity calculation between vectors of different interest tags of users and the uploaders, an uploader list is obtained according to proportions under different interest tags of the users, and the list serves as a candidate set for recommendation to target users after merging and duplicate-removal, wherein N is an integer greater than 1, and the specific evaluation thereof can be set according to requirements.
[0094] Fig. 5 is a view schematically illustrating the structure of the uploader matching device provided by the embodiments of the present invention. As shown in Fig. 5, the uploader matching device 2 provided by the embodiments of the present invention mainly comprises a calculating module 21, an uploader word vector generating module 22, a user word vector generating module 23 and a matching module 24.
[0095] The calculating module 21 is employed for obtaining released video data of uploaders, determining comprehensive score values of the uploaders from one or more dimension feature score(s) according to the released video data, and screening out target uploaders according to the comprehensive score values of the uploaders.
[0096] Specifically, the calculating module 21 includes a first calculating sub-module 211, a second calculating sub-module 212 and a screening sub-module 213, of which the first calculating sub-module 211 is employed for calculating score(s) of one or more dimension feature(s) in released video activity scores of the uploaders, video quality scores of the uploaders, and video verticality scores of the uploaders according to the released video data; the second calculating sub-module 212 is employed for calculating the comprehensive score values of the uploaders according to the one or more dimension feature score(s); and the screening sub-module 213 is employed for selecting the uploaders who rank top N to serve as the target uploaders according to a sequence of the comprehensive score values of the uploaders arranged in a decreasing order, wherein N
is an integer greater than 1.
[0097] Preferably, the first calculating sub-module 211 is employed for:
sorting the number of released videos of the uploaders within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with time decay, mapping the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of' [x1,1] and a range of [x2,1], determining respective weight indices of the number of released videos and the volume of released videos played back, wherein a first weight index xl and a second weight index x2 are each evaluated as a decimal between 0 and 1, thereafter multiplying the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back, and calculating to obtain the released video activity scores of the uploaders;
[0098] sorting the number of sharings, the number of praisings, the number of commentings, a proportion of positive comments, the number of listings as favorites, the number of followings and released video playback integrity rates of released videos of the uploaders within the second preset period respectively combined with time decay, mapping the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively determining respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1, thereafter summating and averaging the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings, subsequently multiplying the summating and averaging result with the respective weight indices of the released video playback integrity rates, and calculating to obtain the video quality scores of the uploaders;
and sorting category proportions of released videos of the uploaders within the second preset period in combination with time decay, mapping the sorted category proportions to a range of [xl 0,11, determining respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1, thereafter multiplying the respective weight indices of the category proportions, and calculating to obtain the video verticality scores of the uploaders.
[0099] The step of obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 is as follows:
[0100] respectively taking various dimension feature scores to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables, taking following degrees of the uploaders after exposure as dependent variables, and employing a RandomForest algorithm and a GBDT
algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
[0101] The second calculating sub-module 212 is employed for: multiplying the released video activity scores, the video quality scores and the video verticality scores, and calculating to obtain the comprehensive score values of the uploaders.
[0102] The uploader word vector generating module 22 is employed for making statistics on the released video data of the target uploaders according to a preset tagging rule, and generating corresponding uploader word vector of one or more video category tag(s).
[0103] The user word vector generating module 23 is employed for obtaining user played-back video data within a first preset period, making statistics on the user played-back video data according to the preset tagging rule, and generating corresponding user word vector of the one or more video category tag(s). Specifically, hotspot videos and miss-clicked videos are eliminated from the user played-back video data, top N target user tags each of whose number of videos occupies a proportion that is not lower than a preset proportion are counted out according to the preset tagging rule, the target user tags are calculated, and corresponding user word vectors of the target user tags are generated, wherein N is an integer greater than 1.
[0104] The matching module 24 is employed for correspondingly matching the corresponding video category tags of the uploader word vector and the user word vector, obtaining an uploader word vector result that has reached a target matching degree with the user, and determining corresponding uploader information according to the uploader word vector result.
[0105] Preferably, the uploader matching device further comprises a data recommending module 25 for recommending the uploader information to the user; and/or pushing a video of the video category tag corresponding to the uploader word vector result to the user.
[0106] As should be noted, when the uploader matching device provided by this embodiment triggers uploader matching, the division into the aforementioned various functional modules is merely by way of example, while it is possible, in actual application, to base on requirements to assign the functions to different functional modules for completion, that is to say, to divide the internal structure of the device into different functional modules to complete the entire or partial functions described above. In addition, the uploader matching device provided by this embodiment pertains to the same conception as the uploader matching method provided by the method embodiment ¨ see the corresponding method embodiment for its specific realization process, while no repetition will be made in this context.
[0107] A preferred mode for the uploader matching method and device provided by the embodiments of the present invention to perform uploader matching business is introduced below. In this preferred mode of execution, a word segmentation tool carries a lexicon therewith, entertainment stars, film and TV drama names, sports stars and team information are additionally added as supplementary lexicon, Netease news, Baidu encyclopedia and Wikipedia obtained by a crawler system constitute a massive corpus, and word segmentation and word vector training are performed with respect to the corpus to finally obtain word vector representation of each word, dimensions of the word vectors are 200 dimensions, as determined by test effect, and normalization is then performed on the vectors.
[0108] Under the aforementioned corpus, TF-IDF training is carried out to obtain IDF values, normalization is then performed, weight enhancement is subsequently performed on the supplementary lexicon as 1, similar to the attention mechanism, more attention is paid to these words.
[0109] See the following Table 1 for video information, in which are carried video id, video title information, classification tags, video tag information, and releasing times, etc. The video information is word-segmented, a word vector table of words is searched, and the word vector representation of the current video is obtained in combination with weighted calculation of an IDF value table (performing normalization).
Table 1 Video Information Table id title cata_name tag_names release time 3714869 Wu lei has athletics athletics, 2019-08-become the European 10:07:32 Mr. Key, Championship, winning odds Wu lei, sports show Espanyol lottery, lottery will bring information surprise in the station, sports new season lottery video collection
[0110] Drawing a portrait of a user is a process of calculating user word vector, and the target user group directed is a group of active users, namely recently active users (having playback records within 7 days lately) with certain volume of playbacks (exceeding 10 videos) within a period lately (such as the recent 30 days). Word vector calculation of the users is particularized according to tag categories, for instance, a user played 100 videos within a period lately, of which there are 60 relevant to athletics, 20 relevant to finance and economics, 15 relevant to amusement, 4 relevant to the society, and 1 relevant to health; during the process of user portrait, a portrait is drawn for the user under tag categories ranking top 3 in the proportions and with the proportions each exceeding 10%, through which method it is made possible to obtain the main interest points of the user, and to remove few miss-clicked operations and hotspot videos that cannot represent the interest points of the user. In this example, athletics occupies 60%, finance and economics occupy 20%, amusement occupies 15%, society occupies 4%, and health occupies 1%, so the portrait is drawn for the user in terms of the three dimensions of athletics, finance and economics, and amusement with respect to the current user, and word vector representations of the corresponding dimensions of the user are calculated.
[0111] During the process of calculating user word vectors under different tag categories of the user, the time decay factor (for instance, the decay period is 5 days, the decay coefficient is 0.95, taking for example a video played back 12 days before the current date, the video crosses two decay periods, and should be decayed by 0.951'2) is combined to calculate the word vector representations of the user.
[0112] The three big dimensions of activity of the uploaders, quality of the uploaders, and verticality of the uploaders are comprehensively considered to facilitate subsequent comprehensive scoring of the qualities of the uploaders.
[0113] The activity indicators of the uploaders are sorted by a combination of the number of videos released in the recent three months with time decay, the sorting result is mapped to a range of [x1,1] (it is avoided that some few uploaders differ from the majority in the order of magnitude, so as to render the dimension meaningless), wherein xl is a decimal between 0 and 1, and is obtained through model learning as a model parameter.
Another indicator of activity is the volumes of videos played backed within the recent three months sorted according to time decay, and mapped to [x2,1]. The aforementioned two indicators are multiplied to obtain scores of the dimension of activity of the uploaders.
[0114] The quality scores of the uploaders are calculated by averaging the scores of such dimensions as sharing, praising, commenting, the proportion of positive comments, listing as favorites and following (that are respectively sorted and thereafter mapped to between a certain variable x and 1, wherein x is a decimal between 0 and 1), and the averaging result is then multiplied with released video playback integrity rates to obtain the scores of the quality dimension.
[0115] The verticality scores of the uploaders are obtained by calculating verticalities of category proportions of released videos within the recent three months, sorted and mapped to between a certain variable x and 1, wherein xis a decimal between 0 and 1.
[0116] While the various dimension scores are calculated, different importance degrees of the dimensions in the calculation of the comprehensive scores are decided by mapping them to the corresponding scoring ranges, namely the weights of the various dimension scores, these parameters are obtained through model training, as shown in the following Table 2.
Table 2 Mapping Datasheet of Various Dimension Scores Parameter xl x2 x3 x4 x5 Meaning sorting of numbers sorting of sorting of sorting of sorting of of videos released in volumes of numbers of numbers of numbers of the recent three released videos sharings, mapped prai sings, commentings, months, mapped to integrally played to [x3,1]
mapped to [x4,1] mapped to [x5,1]
[x1,1] back in the recent three months to before three days, mapped to [x2,1]
Range xl is a decimal x2 is a decimal x3 is a decimal x4 is a decimal x5 is a decimal between 0 and 1 between 0 and 1 between 0 and 1 between 0 and 1 between 0 and 1 Parameter x6 x7 x8 x9 x10 Meaning sorting of sorting of sorting of summarized verticality scoring proportions of numbers of numbers of sorting of of various types positive comments, listings as followings, released video of uploaders mapped to [x6,1] favorites, mapped mapped to [x8,11 playback (sorting of various to [x7,1] integrity rates in category the recent three proportions of months to before videos released in three days, recent three mapped to [x9,1] months, mapped to [x10,1]) Range x6 is a decimal x7 is a decimal x8 is a decimal x9 is a decimal x10 is a decimal between 0 and 1 between 0 and 1 between 0 and 1 between 0 and 1 between 0 and 1
[0117] The various dimension features constructed above serve as independent variables, the following degrees of the uploaders (uploaders exposed to users being followed by users) serve as dependent variables, and the weights of the aforementioned indicators are obtained through modeling and training.
[0118] The comprehensive scores of the qualities of uploaders are obtained by multiplying the three dimension scores in terms of activity indicator, quality and verticality of the uploaders, and these scores are sorted and mapped to between [0,1000] to serve as the comprehensive scores of the qualities of the uploaders. While uploaders are being recommended to users, only the uploaders with higher qualities are selected (for example, uploaders ranking top 600 in the comprehensive scores of qualities are selected).
[0119] The calculation of uploader word vectors (with respect to high-quality uploaders) is based on videos released by uploaders within the recent three months, and is similar to the process of calculating user-particularized word vectors, whereby top 3 in the proportions with each category occupying a proportion greater than 10% are selected according to classification tags of released videos of the uploaders to finely calculate word vector representations (word vector representations according to videos, calculated and obtained in combination with the time decay factor) of the uploaders in a plurality of dimensions.
[0120] Similarities between users and uploaders are calculated through cosine similarity calculation; with respect to the scenario in which a target user is drawn with a portrait having a plurality of tagged dimensions, her/his result set of recommendation of uploaders is constituted according to proportions of the tags of the user, and inversion is finally made according to similarities for recommendation to the user.
[0121] All the aforementioned optional technical solutions are randomly combinable to form optional embodiments of the present invention, to which no repetition is made on a one-by-one basis.
[0122] To sum it up, in comparison with prior-art technology, the uploader matching method and device provided by the embodiments of the present invention achieve the following advantageous effects.
[0123] 1. By sorting out in overall such data as released video information of uploaders and user behaviors, history records of user playbacks and such multi-dimensional information as relevant to the listing as favorites, sharing, praising, commenting and playback integrities of videos released by uploaders are obtained, a comprehensive scoring scheme based on multiple data sources and multi-dimensional variables achieves comprehensive evaluation of uploaders, and high-quality uploader information is extracted therefrom.
[0124] 2. Various dimension weights for evaluating uploader qualities are obtained through model training by corresponding algorithms, and evaluation of uploader qualities is made more precise.
[0125] 3. Uploaders are finely classified according to released video category tags, whereby precision of uploader portraits is enhanced.
[0126] 4. During the process of drawing portraits of users, hotspot videos and possibly miss-clicked videos are eliminated, portraits are drawn for the users by differentiating different interest tags, result sets are proportionally formed, and precision in recommending result sets is enhanced.
[0127] 5. Time decay is taken into consideration in the processes of calculating user vector and calculating uploader vector, whereby transfer of interest points is enhanced.
[0128] 6. By matching the high-quality uploader word vector with the user word vector of a precise user portrait, high-quality uploaders are finally recommended to target users with high matching degree, by following uploaders similar to themselves, users are facilitated to timely watch high-quality videos, whereby satisfaction of the users is enhanced, comparison report indicators are obtained through AB test, whereby CTR, user playback volume and average playback integrity are enhanced, and user experience is enhanced as a whole.
[0129] As understandable by persons ordinarily skilled in the art, realization of the entire or partial steps of the aforementioned embodiments can be completed by hardware, or by a program instructing relevant hardware, the program can be stored in a computer-readable storage medium, and the storage medium can be a read-only memory, a magnetic disk, or an optical disk, etc.
[0130] The embodiments of the present application are described with reference to flowcharts and/or block diagrams of the method, device (system), and computer program product embodied in the embodiments of the present application. As should be understood, each flow and/or block in the flowcharts and/or block diagrams, and any combination of flow and/or block in the flowcharts and/or block diagrams can be realized by computer program instructions. These computer program instructions can be supplied to a general computer, a dedicated computer, an embedded processor or a processor of any other programmable data processing device to form a machine, so that the instructions executed by the computer or the processor of any other programmable data processing device generate a device for realizing the functions designated in one or more flow(s) of the flowcharts and/or one or more block(s) of the block diagrams.
[0131] These computer program instructions can also be stored in a computer-readable memory enabling a computer or any other programmable data processing device to operate by a specific mode, so that the instructions stored in the computer-readable memory generate a product containing instructing means, and this instructing means realizes the functions designated in one or more flow(s) of the flowcharts and/or one or more block(s) of the block diagrams.
[0132] These computer program instructions can also be loaded onto a computer or any other programmable data processing device, so as to execute a series of operations and steps on the computer or the any other programmable device to generate computer-realized processing, so that the instructions executed on the computer or the any other programmable device provide steps for realizing the functions designated in one or more flow(s) of the flowcharts and/or one or more block(s) of the block diagrams.
[0133] Although preferred embodiments in the embodiments of the present application have been described so far, it is still possible for persons skilled in the art to make additional modifications and amendments to these embodiments upon learning of the basic inventive conception. Accordingly, the attached Claims are meant to cover the preferred embodiments and all modifications and amendments that fall within the scope of the embodiments of the present application.
[0134] Apparently, persons skilled in the art can make various amendments and modifications to the present invention without departing from the spirit and scope of the present invention.
Thus, if such amendments and modifications to the present invention fall within the Claims of the present invention and equivalent technology, the present invention is also meant to cover these amendments and modifications.
[0135] What is described above is merely directed to preferred embodiments of the present invention, and they are not meant to restrict the present invention. Any amendment, equivalent replacement and improvement makeable within the spirit and scope of the present invention shall all be covered within the protection scope of the present invention.

Claims (95)

Claims:
1. A device for uploader matching comprising:
a calculating module configured to:
obtain a released video data of at least one uploader;
determine at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data; and screen out at least one target uploader according to the at least one comprehensive score value of the at least one uploader;
an uploader word vector generating module configured to:
make statistics on the released video data of the at least one target uploader according to a preset tagging rule; and generate a corresponding uploader word vector of at least one video category tag;
a user word vector generating module configured to:
obtain a user played-back video data within a first preset period;
make statistics on the user played-back video data according to the preset tagging rule; and generate a corresponding user word vector of the at least one video category tag; and a matching module configured to:

Date Reçue/Date Received 2023-11-21 match the corresponding of the at least one video category tag of the at least one uploader word vector and a user word vector;
obtain an uploader word vector result that has reached a target matching degree with the at least one uploader word vector; and determine corresponding uploader information according to the uploader word vector result.
2. The device of claim 1 wherein the calculating module further comprises:
a first calculating sub-module configured to calculate at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
a second calculating sub-module configured to calculate the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score; and a screening sub-module configured to select the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one.
3. The device of claim 2 wherein the first calculating sub-module configured to:
check an external order information of each target order according to a preset label generating rule; and obtain a checking result of the each target order.
4. The device according to any one of claims 2 to 3 wherein the first calculating submodule is further configured to:

Date Recue/Date Received 2023-11-21 sort a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;
map the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein x 1 is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determine respective weight indices of the number of released videos and the volume of released videos played back;
multiply the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculate to obtain the released video activity scores of the at least one uploader;
sort one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;
map the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;

Date Recue/Date Received 2023-11-21 determine respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summate and average the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;
multiply a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculate to obtain the video quality scores of the at least one uploader;
sort category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
map the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determine respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
multiply the respective weight indices of the category proportions; and calculate to obtain the video verticality scores of the at least one uploader.
5.
The device of claim 4 wherein obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:

Date Recue/Date Received 2023-11-21 taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and employing a RandomForest algorithm and a gradient boosted decision tree (GBDT) algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
6. The device of any one of claims 2 to 5 wherein the second calculating sub-module is configured to:
multiply the released video activity scores, the video quality scores and the video verticality scores; and calculate to obtain the at least one comprehensive score value of the at least one uploader.
7. The device of any one of claims 1 to 6 wherein the user word vector generating module is further configured to:
eliminate one or more of hotspot videos and miss-clicked videos from the user played-back video data;
count target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein the preset proportion is greater than one;
Date Recue/Date Received 2023-11-21 calculate the target user tags; and generate corresponding user word vectors of the target user tags.
8. The device of any one of claims 1 to 7 further comprising a data recommending module configured to recommend the uploader information to the user.
9. The device of any one of claims 1 to 8 further comprising the data recommending module configured to push a video of the at least one video category tag corresponding to the uploader word vector result to the user.
10. The device of any one of claims 1 to 9 wherein one or more of activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.
11. A system for uploader matching comprising:
a calculating module configured to:
obtain a released video data of at least one uploader;
determine at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data; and screen out at least one target uploader according to the at least one comprehensive score values of the at least one uploader;
an uploader word vector generating module configured to:
make statistics on the released video data of the at least one target uploader according to a preset tagging rule; and Date Recue/Date Received 2023-11-21 generate a corresponding uploader word vector of at least one video category tag;
a user word vector generating module configured to:
obtain a user played-back video data within a first preset period;
make statistics on the user played-back video data according to the preset tagging rule; and generate a corresponding user word vector of the at least one video category tag; and a matching module configured to:
match the corresponding of the at least one video category tag of the at least one uploader word vector and a user word vector;
obtain an uploader word vector result that has reached a target matching degree with the user; and determine corresponding uploader information according to the uploader word vector result.
12. The system of claim 11 wherein the calculating module further comprises:
a first calculating sub-module configured to calculate at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
a second calculating sub-module configured to calculate the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score; and Date Recue/Date Received 2023-11-21 a screening sub-module configured to select the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one.
13. The system of claim 12 wherein the first calculating sub-module configured to:
check an extemal order information of each target order according to a preset label generating rule; and obtain a checking result of the each target order.
14. The system according to any one of claims 12 to 13 wherein the first calculating submodule is further configured to:
sort a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;
map the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein x 1 is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determine respective weight indices of the number of released videos and the volume of released videos played back;
multiply the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculate to obtain the released video activity scores of the at least one uploader;

Date Recue/Date Received 2023-11-21 sort one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;
map the sorted number of sharings, number of praisings, number of commenfings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;
determine respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summate and average the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;
multiply a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculate to obtain the video quality scores of the at least one uploader;

Date Recue/Date Received 2023-11-21 sort category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
map the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determine respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
multiply the respective weight indices of the category proportions; and calculate to obtain the video verticality scores of the at least one uploader.
15.
The system of claim 14 wherein obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:
taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and employing a RandomForest algorithm and a GBDT algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
Date Reçue/Date Received 2023-11-21
16. The system of any one of claims 12 to 15 wherein the second calculating sub-module is configured to:
multiply the released video activity scores, the video quality scores and the video verticality scores; and calculate to obtain the at least one comprehensive score value of the at least one uploader.
17. The system of any one of claims 11 to 16 wherein the user word vector generating module is further configured to:
eliminate one or more of hotspot videos and miss-clicked videos from the user played-back video data;
count target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein a preset proportion is greater than one;
calculate the target user tags; and generate corresponding user word vectors of the target user tags.
18. The system of any one of claims 11 to 17 further comprising a data recommending module configured to recommend the uploader information to the user.
19. The system of any one of claims 11 to 18 further comprising the data recommending module configured to push a video of the at least one video category tag corresponding to the uploader word vector result to the user.
20. The system of any one of claims 11 to 19 wherein one or more of activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.

Date Recue/Date Received 2023-11-21
21. A method for uploader matching comprising:
obtaining a released video data of at least one uploader;
determining at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data; and screening out at least one target uploader according to the at least one comprehensive score value of the at least one uploader;
making statistics on the released video data of the at least one target uploader according to a preset tagging rule; and generating a corresponding uploader word vector of at least one video category tag;
obtaining a user played-back video data within a first preset period;
making statistics on the user played-back video data according to the preset tagging rule; and generating a corresponding user word vector of the at least one video category tag;
and matching the corresponding of the at least one video category tag of the at least one uploader word vector and a user word vector;
obtaining an uploader word vector result that has reached a target matching degree with the user; and determining corresponding uploader information according to the uploader word vector result.
22. The method of claim 21 further comprising:

Date Recue/Date Received 2023-11-21 calculating at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
calculating the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score; and selecting the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one.
23. The method of claim 22 further comprising:
checking an external order information of each target order according to a preset label generating rule; and obtaining a checking result of the each target order.
24. The method according to any one of claims 22 to 23 further comprising:
sorting a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;
mapping the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein xl is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determining respective weight indices of the number of released videos and the volume of released videos played back;

Date Recue/Date Received 2023-11-21 multiplying the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculating to obtain the released video activity scores of the at least one uploader;
sorting one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;
mapping the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,11, a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;
determining respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integiity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summating and averaging the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;

Date Recue/Date Received 2023-11-21 multiplying a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculating to obtain the video quality scores of the at least one uploader;
sorting category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
mapping the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determining respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
multiplying the respective weight indices of the category proportions; and calculating to obtain the video verticality scores of the at least one uploader.
25.
The method of claim 24 wherein obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:
taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and Date Recue/Date Received 2023-11-21 employing a RandomForest algorithm and a GBDT algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
26. The method of any one of claims 22 to 25 further comprising:
multiplying the released video activity scores, the video quality scores and the video verticality scores; and calculating to obtain the at least one comprehensive score value of the at least one uploader.
27. The method of any one of claims 21 to 26 further comprising:
eliminating one or more of hotspot videos and miss-clicked videos from the user played-back video data;
counting target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein a preset proportion is greater than one;
calculating the target user tags; and generating corresponding user word vectors of the target user tags.
28. The method of any one of claims 21 to 27 further comprising recommending the uploader information to the user.
29. The method of any one of claims 21 to 28 further comprising pushing a video of the at least one video category tag corresponding to the uploader word vector result to the user.

Date Recue/Date Received 2023-11-21
30. The method of any one of claims 21 to 29 wherein one or more of the activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.
31. A computer equipment for uploader matching comprising a computer readable physical memory and a processor communicatively connected to the memory wherein the processor is configured to execute a computer-executable instructions stored on the memory and wherein the processor when executing the computer-executable instructions is configured to:
obtain a released video data of at least one uploader determine at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data; and screen out at least one target uploader according to the at least one comprehensive score value of the at least one uploader;
make statistics on the released video data of the at least one target uploader according to a preset tagging rule; and generate a corresponding uploader word vector of at least one video category tag;
obtain a user played-back video data within a first preset period;
make statistics on the user played-back video data according to the preset tagging rule; and generate a corresponding user word vector of the at least one video category tag; and match the corresponding of the at least one video category tag of the at least one uploader word vector and a user word vector;

Date Recue/Date Received 2023-11-21 obtain an uploader word vector result that has reached a target matching degree with the user; and determine corresponding uploader information according to the uploader word vector result.
32. The computer equipment of claim 31 wherein the processor is further configured to:
calculate at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
calculate the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score; and select the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one.
33. The computer equipment of claim 32 wherein the processor is further configured to:
check an external order information of each target order according to a preset label generating rule; and obtain a checking result of the each target order.
34. The computer equipment according to any one of claims 32 to 33 wherein the processor is further configured to:
sort a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;

Date Recue/Date Received 2023-11-21 map the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein x I is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determine respective weight indices of the number of released videos and the volume of released videos played back;
multiply the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculate to obtain the released video activity scores of the at least one uploader;
sort one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;
map the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;

Date Recue/Date Received 2023-11-21 determine respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summate and average the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;
multiply a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculate to obtain the video quality scores of the at least one uploader;
sort category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
map the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determine respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
multiply the respective weight indices of the category proportions; and calculate to obtain the video verticality scores of the at least one uploader.
35.
The computer equipment of claim 34 wherein obtaining the first weight index x 1, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:
Date Recue/Date Received 2023-11-21 taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and employing a RandomForest algorithm and a GBDT algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
36. The computer equipment of any one of claims 32 to 35 wherein the processor is further configured to:
multiply the released video activity scores, the video quality scores and the video verticality scores; and calculate to obtain the at least one comprehensive score value of the at least one uploader.
37. The computer equipment of any one of claims 31 to 36 wherein the processor is further configured to:
eliminate one or more of hotspot videos and miss-clicked videos from the user played-back video data;
count target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein a preset proportion is greater than one;

Date Recue/Date Received 2023-11-21 calculate the target user tags; and generate corresponding user word vectors of the target user tags.
38. The computer equipment of any one of claims 31 to 37 wherein the processor is further configured to recommend the uploader information to the user.
39. The computer equipment of any one of claims 31 to 38 wherein the processor is further configured to push a video of the at least one video category tag corresponding to the uploader word vector result to the user.
40. The computer equipment of any one of claims 31 to 39 wherein one or more of the activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.
41. A computer readable physical memory having stored upon it a computer-executable instructions when executed by a computer configured to:
obtain a released video data of at least one uploader;
determine at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data; and screen out at least one target uploader according to the at least one comprehensive score value of the at least one uploader;
make statistics on the released video data of the at least one target uploader according to a preset tagging rule; and generate a corresponding uploader word vector of at least one video category tag;
obtain a user played-back video data within a first preset period;

Date Recue/Date Received 2023-11-21 make statistics on the user played-back video data according to the preset tagging rule; and generate a corresponding user word vector of the at least one video category tag; and match the corresponding of the at least one video category tag of the at least one uploader word vector and a user word vector;
obtain an uploader word vector result that has reached a target matching degree with the user; and determine corresponding uploader information according to the uploader word vector result.
42. The memory of claim 41 wherein the computer is further configured to:
calculate at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
calculate the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score; and select the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one.
43. The memory of claim 42 wherein the computer is further configured to:
check an external order information of each target order according to a preset label generating rule; and Date Recue/Date Received 2023-11-21 obtain a checking result of the each target order.
44.
The memory according to any one of claims 42 to 43 wherein the processor is further configured to:
sort a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;
map the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein x I is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determine respective weight indices of the number of released videos and the volume of released videos played back;
multiply the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculate to obtain the released video activity scores of the at least one uploader;
sort one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;

Date Recue/Date Received 2023-11-21 map the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;
determine respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summate and average the respective weight indices of the number of sharings, the number of praisings, the ni mber of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;
multiply a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculate to obtain the video quality scores of the at least one uploader;
sort category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
map the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determine respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
Date Recue/Date Received 2023-11-21 multiply the respective weight indices of the category proportions; and calculate to obtain the video verticality scores of the at least one uploader.
45. The memory of claim 44 wherein obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:
taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and employing a RandomForest algorithm and a GBDT algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
46. The memory of any one of claims 42 to 45 wherein the computer is further configured to:
multiply the released video activity scores, the video quality scores and the video verticality scores; and calculate to obtain the at least one comprehensive score value of the at least one uploader.
47. The memory of any one of claims 41 to 46 wherein the computer is further configured to:

Date Recue/Date Received 2023-11-21 eliminate one or more of hotspot videos and miss-clicked videos from the user played-back video data;
count target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein a preset proportion is greater than one;
calculate the target user tags; and generate corresponding user word vectors of the target user tags.
48. The memory of any one of claims 41 to 47 wherein the computer is further configured to recommend the uploader information to the user.
49. The memory of any one of claims 41 to 48 wherein the computer is further configured to push a video of the at least one video category tag corresponding to the uploader word vector result to the user.
50. The memory of any one of claims 41 to 49 wherein one or more of the activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.
51. A device for uploader matching comprising:
a calculating module configured to:
obtain a released video data of at least one uploader;
determine at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data; and Date Recue/Date Received 2023-11-21 screen out at least one target uploader according to the at least one comprehensive score value of the at least one uploader;
wherein the calculating module further comprises:
a first calculating sub-module configured to calculate at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
a second calculating sub-module configured to calculate the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score; and a screening sub-module configured to select the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one;
an uploader word vector generating module configured to:
make statistics on the released video data of the at least one target uploader according to a preset tagging rule; and generate a corresponding uploader word vector of at least one video category tag;
a user word vector generating module configured to:
obtain a user played-back video data within a first preset period;

Date Recue/Date Received 2023-11-21 make statistics on the user played-back video data according to the preset tagging rule; and generate a corresponding user word vector of the at least one video category tag; and a matching module configured to:
match the corresponding of the at least one video category tag of the at least one uploader word vector and a user word vector;
obtain an uploader word vector result that has reached a target matching degree with the user; and determine corresponding uploader information according to the uploader word vector result.
52. The device of claim 51 wherein the first calculating sub-module configured to:
check an external order information of each target order according to a preset label generating rule; and obtain a checking result of the each target order.
53. The device according to any one of claims 51 to 52 wherein the first calculating submodule is further configured to:
sort a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;

Date Recue/Date Received 2023-11-21 map the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein x I is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determine respective weight indices of the number of released videos and the volume of released videos played back;
multiply the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculate to obtain the released video activity scores of the at least one uploader;
sort one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;
map the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;
Date Recue/Date Received 2023-11-21 determine respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summate and average the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;
multiply a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculate to obtain the video quality scores of the at least one uploader;
sort category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
map the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determine respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
multiply the respective weight indices of the category proportions; and calculate to obtain the video verticality scores of the at least one uploader.
54.
The device of claim 53 wherein obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:

Date Reçue/Date Received 2023-11-21 taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and employing a RandomForest algorithm and a GBDT algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
55. The device of any one of claims 51 to 54 wherein the second calculating sub-module is configured to:
multiply the released video activity scores, the video quality scores and the video verticality scores; and calculate to obtain the at least one comprehensive score value of the at least one uploader.
56. The device of any one of claims 51 to 55 wherein the user word vector generating module is further configured to:
eliminate one or more of hotspot videos and miss-clicked videos from the user played-back video data;
count target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein a preset proportion is greater than one;

Date Recue/Date Received 2023-11-21 calculate the target user tags; and generate corresponding user word vectors of the target user tags.
57. The device of any one of claims 51 to 56 further comprising a data recommending module configured to recommend the uploader information to the user.
58. The device of any one of claims 51 to 57 further comprising the data recommending module configured to push a video of the at least one video category tag corresponding to the uploader word vector result to the user.
59. The device of any one of claims 51 to 58 wherein one or more of activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.
60. A system for uploader matching comprising:
a calculating module configured to:
obtain a released video data of at least one uploader;
determine at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data; and screen out at least one target uploader according to the comprehensive score values of the uploader;
wherein the calculating module further comprises:

Date Recue/Date Received 2023-11-21 a first calculating sub-module configured to calculate at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
a second calculating sub-module configured to calculate the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score; and a screening sub-module configured to select the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploaderthe at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one;
an uploader word vector generating module configured to:
make statistics on the released video data of the at least one target uploader according to a preset tagging rule; and generate a corresponding uploader word vector of at least one video category tag;
a user word vector generating module configured to:
obtain a user played-back video data within a first preset period;
make statistics on the user played-back video data according to the preset tagging rule; and generate a corresponding user word vector of the at least one video category tag; and Date Recue/Date Received 2023-11-21 a matching module configured to:
match the corresponding of the at least one video category tag of the at least one uploader word vector and a user word vector;
obtain an uploader word vector result that has reached a target matching degree with the user; and determine corresponding uploader infonnation according to the uploader word vector result.
61. The system of claim 60 wherein the first calculating sub-module configured to:
check an external order information of each target order according to a preset label generating nile; and obtain a checking result of the each target order.
62. The system according to any one of claims 60 to 61 wherein the first calculating submodule is further configured to:
sort a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;
map the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein x 1 is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determine respective weight indices of the number of released videos and the volume of released videos played back;
Date Recue/Date Received 2023-11-21 multiply the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculate to obtain the released video activity scores of the at least one uploader;
sort one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;
map the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;
determine respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integiity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summate and average the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;

Date Reçue/Date Received 2023-11-21 multiply a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculate to obtain the video quality scores of the at least one uploader;
sort category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
map the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determine respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
multiply the respective weight indices of the category proportions; and calculate to obtain the video verticality scores of the at least one uploader.
63.
The system of claim 62 wherein obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:
taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and Date Recue/Date Received 2023-11-21 employing a RandomForest algorithm and a GBDT algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
64. The system of any one of claims 60 to 63 wherein the second calculating sub-module is configured to:
multiply the released video activity scores, the video quality scores and the video verticality scores; and calculate to obtain the at least one comprehensive score value of the at least one uploader.
65. The system of any one of claims 60 to 64 wherein the user word vector generating module is further configured to:
eliminate one or more of hotspot videos and miss-clicked videos from the user played-back video data;
count target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein a preset proportion is greater than one;
calculate the target user tags; and generate corresponding user word vectors of the target user tags.
66. The system of any one of claims 60 to 65 further comprising a data recommending module configured to recommend the uploader information to the user.

Date Recue/Date Received 2023-11-21
67. The system of any one of claims 60 to 66 further comprising the data recommending module configured to push a video of the at least one video category tag corresponding to the uploader word vector result to the user.
68. The system of any one of claims 60 to 67 wherein one or more of activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.
69. A method for uploader matching comprising:
obtaining a released video data of at least one uploader;
determining at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data;
calculating at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
calculating the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score; and selecting the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one;
screening out at least one target uploader according to the at least one comprehensive score value of the at least one uploader;
making statistics on the released video data of the at least one target uploader according to a preset tagging rule; and Date Recue/Date Received 2023-11-21 generating a corresponding uploader word vector of at least one video category tag;
obtaining a user played-back video data within a first preset period;
making statistics on the user played-back video data according to the preset tagging rule; and generating a corresponding user word vector of the at least one video category tag;
and matching the corresponding of the at least one video category tag of the at least one uploader word vector and a user word vector;
obtaining an uploader word vector result that has reached a target matching degree with the user; and determining corresponding uploader information according to the uploader word vector result.
70. The method of claim 69 further comprising:
checking an external order information of each target order according to a preset label generating rule; and obtaining a checking result of the each target order.
71. The method according to any one of claims 69 to 70 further comprising:
sorting a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;
Date Recue/Date Received 2023-11-21 mapping the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein xl is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determining respective weight indices of the number of released videos and the volume of released videos played back;
multiplying the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculating to obtain the released video activity scores of the at least one uploader;
sorting one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;
mapping the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;

Date Recue/Date Received 2023-11-21 determining respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summating and averaging the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;
multiplying a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculating to obtain the video quality scores of the at least one uploader;
sorting category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
mapping the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determining respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
multiplying the respective weight indices of the category proportions; and calculating to obtain the video verticality scores of the at least one uploader.
72.
The method of claim 71 wherein obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:
72 Date Recue/Date Received 2023-11-21 taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and employing a RandomForest algorithm and a GBDT algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
73. The method of any one of claims 69 to 72 further comprising:
multiplying the released video activity scores, the video quality scores and the video verticality scores; and calculating to obtain the at least one comprehensive score value of the at least one uploader.
74. The method of any one of claims 69 to 73 further comprising:
eliminating one or more of hotspot videos and miss-clicked videos from the user played-back video data;
counting target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein a preset proportion is greater than one;
calculating the target user tags; and Date Recue/Date Received 2023-11-21 generating corresponding user word vectors of the target user tags.
75. The method of any one of claims 69 to 74 further comprising recommending the uploader information to the user.
76. The method of any one of claims 69 to 75 further comprising pushing a video of the video category tag corresponding to the uploader word vector result to the user.
77. The method of any one of claims 69 to 76 wherein one or more of the activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.
78. A computer equipment for uploader matching comprising a computer readable physical memory and a processor communicatively connected to the memory wherein the processor is configured to execute a computer-executable instructions stored on the memory and wherein the processor when executing the computer-executable instructions is configured to:
obtain a released video data of at least one uploader;
determine at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data;
calculate at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
calculate the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score;

Date Recue/Date Received 2023-11-21 select the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one;
screen out at least one target uploader according to the comprehensive score values of the uploader;
make statistics on the released video data of the at least one target uploader according to a preset tagging rule; and generate a corresponding uploader word vector of at least one video category tag;
obtain a user played-back video data within a first preset period;
make statistics on the user played-back video data according to the preset tagging rule; and generate a corresponding user word vector of the at least one video category tag; and match the corresponding of the at least one video category tag of the at least one uploader word vector and the user word vector;
obtain an uploader word vector result that has reached a target matching degree with the user; and determine corresponding uploader information according to the uploader word vector result.
79. The computer equipment of claim 78 wherein the processor is further configured to:
check an external order information of each target order according to a preset label generating rule; and obtain a checking result of the each target order.
Date Recue/Date Received 2023-11-21
80.
The computer equipment according to any one of claims 78 to 79 wherein the processor is further configured to:
sort a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;
map the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein x 1 is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determine respective weight indices of the number of released videos and the volume of released videos played back;
multiply the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculate to obtain the released video activity scores of the at least one uploader;
sort one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;
map the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;

Date Recue/Date Received 2023-11-21 determine respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summate and average the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;
multiply a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculate to obtain the video quality scores of the at least one uploader;
sort category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
map the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determine respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
multiply the respective weight indices of the category proportions; and calculate to obtain the video verticality scores of the at least one uploader.
81.
The computer equipment of claim 80 wherein obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:

Date Recue/Date Received 2023-11-21 taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and employing a RandomForest algorithm and a GBDT algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
82. The computer equipment of any one of claims 78 to 81 wherein the processor is further configured to:
multiply the released video activity scores, the video quality scores and the video verticality scores; and calculate to obtain the at least one comprehensive score value of the at least one uploader.
83. The computer equipment of any one of claims 78 to 82 wherein the processor is further configured to:
eliminate one or more of hotspot videos and miss-clicked videos from the user played-back video data;
count target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein a preset proportion is greater than one;

Date Recue/Date Received 2023-11-21 calculate the target user tags; and generate corresponding user word vectors of the target user tags.
84. The computer equipment of any one of claims 78 to 83 wherein the processor is further configured to recommend the uploader information to the user.
85. The computer equipment of any one of claims 78 to 84 wherein the processor is further configured to push a video of the at least one video category tag corresponding to the uploader word vector result to the user.
86. The computer equipment of any one of claims 78 to 85 wherein one or more of the activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.
87. A computer readable physical memory having stored upon it a computer-executable instructions when executed by a computer configured to:
obtain a released video data of at least one uploader;
determine at least one comprehensive score value of the at least one uploader from at least one dimension feature score according to the released video data;
calculate at least one score of one or more of at least one dimension feature in released video activity scores of the at least one uploader, video quality scores of the at least one uploader, and video verticality scores of the at least one uploader according to the released video data;
calculate the at least one comprehensive score value of the at least one uploader according to the one or more dimension feature score; and Date Recue/Date Received 2023-11-21 select the at least one uploader who rank above a threshold to serve as the at least one target uploader according to a sequence of the at least one comprehensive score value of the at least one uploader arranged in a decreasing order, wherein the threshold is an integer greater than one;
screen out at least one target uploader according to the comprehensive score values of the uploader;
make statistics on the released video data of the at least one target uploader according to a preset tagging rule; and generate a corresponding uploader word vector of at least one video category tag;
obtain a user played-back video data within a first preset period;
make statistics on the user played-back video data according to the preset tagging rule; and generate a corresponding user word vector of the at least one video category tag; and match the corresponding of the at least one video category tag of the at least one uploader word vector and a user word vector;
obtain an uploader word vector result that has reached a target matching degree with the user; and determine corresponding uploader information according to the uploader word vector result.
88. The memory of claim 87 wherein the computer is further configured to:
check an external order information of each target order according to a preset label generating rule; and obtain a checking result of the each target order.
Date Recue/Date Received 2023-11-21
89. The memory any one of claims 87 to 88 wherein the processor is further configured to:
sort a number of released videos of the at least one uploader within a second preset period, and a volume of released videos played back within the second preset period respectively in combination with a time decay;
map the sorted number of released videos and the sorted volume of released videos played back within the second preset period to a range of [x1,1] and a range of [x2,1], wherein x I is a first weight index and x2 is a second weight index each evaluated as a decimal between 0 and 1;
determine respective weight indices of the number of released videos and the volume of released videos played back;
multiply the respective weight indices of the number of released videos with the respective weight indices of the volume of released videos played back;
calculate to obtain the released video activity scores of the at least one uploader;
sort one or more of a number of sharings, a number of praisings, a number of commentings, a proportion of positive comments, a number of listings as favorites, a number of followings and released video playback integrity rates of released videos of the at least one uploader within the second preset period respectively combined with the time decay;
map the sorted number of sharings, number of praisings, number of commentings, proportion of positive comments, number of listings as favorites, number of followings and released video playback integrity rates to a range of [x3,1], a range of [x4,1], a range of [x5,1], a range of [x6,1], a range of [x7,1], a range of [x8,1] and a range of [x9,1], respectively wherein a x3 is a third weight index, x4 is a fourth weight index, x5 is a fifth weight index, x6 is a sixth weight index, x7 is a seventh weight index, x8 is an eighth weight index and x9 a ninth weight index;

Date Recue/Date Received 2023-11-21 determine respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites, the number of followings and the released video playback integrity rates, wherein a third weight index x3, a fourth weight index x4, a fifth weight index x5, a sixth weight index x6, a seventh weight index x7, an eighth weight index x8 and a ninth weight index x9 are each evaluated as a decimal between 0 and 1;
summate and average the respective weight indices of the number of sharings, the number of praisings, the number of commentings, the proportion of positive comments, the number of listings as favorites and the number of followings;
multiply a result of the summating and a result of the averaging with the respective weight indices of the released video playback integrity rates;
calculate to obtain the video quality scores of the at least one uploader;
sort category proportions of released videos of the at least one uploader within the second preset period in combination with the time decay;
map the sorted category proportions to a range of [x10,1] wherein x10 is a tenth weight;
determine respective weight indices of the category proportions, wherein a tenth weight index x10 is evaluated as a decimal between 0 and 1;
multiply the respective weight indices of the category proportions; and calculate to obtain the video verticality scores of the at least one uploader.
90.
The memory of claim 89 wherein obtaining the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 comprises:

Date Recue/Date Received 2023-11-21 taking, respectively, at least one dimension feature score to which the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10 correspond as independent variables;
taking at least one following degree of the at least one uploader after exposure as dependent variables; and employing a RandomForest algorithm and a GBDT algorithm to calculate the first weight index xl, the second weight index x2, the third weight index x3, the fourth weight index x4, the fifth weight index x5, the sixth weight index x6, the seventh weight index x7, the eighth weight index x8, the ninth weight index x9 and the tenth weight index x10.
91. The memory of any one of claims 87 to 90 wherein the computer is further configured to:
multiply the released video activity scores, the video quality scores and the video verticality scores; and calculate to obtain the at least one comprehensive score value of the at least one uploader.
92. The memory of any one of claims 87 to 91 wherein the computer is further configured to:
eliminate one or more of hotspot videos and miss-clicked videos from the user played-back video data;
count target user tags whose number of videos occupies a proportion that is not lower than a preset proportion according to the preset tagging rule wherein a preset proportion is greater than one;
calculate the target user tags; and Date Recue/Date Received 2023-11-21 generate corresponding user word vectors of the target user tags.
93. The memory of any one of claims 87 to 92 wherein the computer is further configured to recommend the uploader information to the user.
94. The memory of any one of claims 87 to 93 wherein the computer is further configured to push a video of the at least one video category tag corresponding to the uploader word vector result to the user.
95. The memory of any one of claims 87 to 94 wherein one or more of the activity of the at least one uploader, quality of the at least one uploader, and verticality of the at least one uploader are comprehensively considered to facilitate subsequent comprehensive scoring of a quality of the at least one uploader.

Date Recue/Date Received 2023-11-21
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