TWI626847B - System and method for video with personalized weighted rating scores - Google Patents

Abstract

A system and method for personalizing audio-visual data scoring comprehensive weighting information includes: a client-level scoring record integration server for obtaining a user's video score data for a video and audio, to the user's data and the video and audio The scoring data is integrated into the client scoring data; the interpreting data collecting server is configured to connect an external plurality of institutions to obtain the scoring data of the plurality of institutions for the audiovisual, and integrate the scoring data of the video into the audio and video interpretation data; The scoring and weighting analysis computing server is configured to receive the client rating data and the video interpretation data to calculate the video score result and the video sounding recommendation through a relationship factor difference weighted analysis algorithm.

Description

System and method for personalized weighted information of personalized audio and video data scores

The invention relates to a video material data scoring technology, in particular to a system and method for personalizing video and audio data scoring comprehensive weighting information.

In recent years, the rapid development of the Internet has made people want to enter the cinema or choose to watch audio and video on-line movies. At the same time, they will be willing to pay for tickets or subscriptions based on the evaluations given by film reviews or film rating agencies. Therefore, more and more websites that provide audio-visual introduction, commentary or scoring services have also emerged. For example, the Internet movie database (English: Internet Movie Database, IMDb), which has developed for a long time in Europe and America, is more biased to the audience. The favorite Rotten Potato (Rotten Tomato Index), the mainland China users are more resident of the Dou Do website and the Taiwanese netizens love Yahoo! Chimo movies, etc., have their own evaluation units and calculation analysis methods.

However, when the user refers to the above-mentioned audio and video introduction scoring website, it is often possible to use the unit calculation problem, such as the most common website using the percentage system or the system, or even the use of the five-point rating as the audio evaluation. Indicators. This is easy to confuse the user. It is not accurate to measure the standard of the heart, and it is not easy to make a choice by evaluation. The other side In addition, the main prevailing areas of the scoring websites and the different levels of preferences of the scorers due to the background of life culture sometimes lead to a gap in taste preferences, which prevents users from discovering good films that may be hidden.

In addition, the online subscription service for on-demand audio and video subscriptions is often only sorted and recommended to consumers using the scores of a single rating agency. There is no integrated weighted ranking of the scores of major organizations. Display, so consumers are not easy to recognize the differences in tastes, types and differences between different organizations, and may lead to a low rate of high-quality audio and video subscriptions that are really suitable for the consumer in the service, which is a lose for both service providers and consumers. situation.

Therefore, how can we use the integration of the scores of major institutions and according to the preferences and habits of consumers, use this to analyze the difference of intelligence and find out the personalized ranking recommendations for high-quality audio and video content suitable for consumers to achieve the satisfaction of viewing videos. The biggest benefit of the promotion and revenue is the technical problem to be solved in this application.

In order to overcome the deficiencies of the prior art, the present invention provides a system for personalizing video and audio data scoring integrated weighting information, which comprises: a client device, including a client scoring module and a client display module, the client rating The module is configured to receive a user's audio and video score data for a video and audio; the client score record integration server is configured to receive the video score data of the client device, to use the user's data and the video score data. Integrating client-side scoring data; interpreting a data collecting server for connecting a plurality of organizations to obtain scoring data of the plurality of institutions for the video and audio, Integrating the audio and video score data into video and audio interpretation data; and the scoring and weighting analysis calculation server is configured to receive the client rating data and the audiovisual interpretation data to pass a relationship factor difference corresponding to the type of the relationship factor The weighted analysis algorithm calculates and obtains the video score result and the video sequence recommendation corresponding to the video and audio, and provides the client display module to read the video score result and the video sequence recommendation to display the video score result and the video sequence recommendation.

The present invention provides a method for personalizing video and audio data to score comprehensive weighted information, which comprises: obtaining a video score data of a user for an audio and video, and integrating the user's data and the video score data into client rating data; The client rating data and a video interpretation data integrated by the plurality of institutions of the audiovisual score data are calculated by a relationship factor difference weighting analysis algorithm corresponding to the type of the relationship factor, and the difference weights corresponding to the plurality of institutions are obtained. And outputting the difference weight corresponding to the plurality of institutions as a humanized relationship factor weight table according to the type of the relationship factor.

It can be seen from the above that the present invention is obtained by scoring and calculating the audio-visual interpretation data and the personalized relationship factor weights integrated by the scores of the audio-visual scores by a plurality of institutions, and obtaining the personalized video score results for each video and audio, and The results of the audio and video scoring are sorted to generate personalized audio and video ranking recommendations. Therefore, the present invention solves the problem that the user can only make a selection reference through the rating of a single institution, and can also present himself and different scoring organizations. The difference between taste and type, in order to get the best sorting recommendation.

100‧‧‧ Interpretation data collection server

110‧‧‧Institutional scoring data acquisition module

120‧‧‧Data Conversion Standardization Module

130‧‧‧Data Integration Pairing Module

140‧‧‧ storage unit

141‧‧‧ audio and video interpretation materials

142‧‧‧Server log

150‧‧‧storage unit access interface

200‧‧‧Client score record integration server

210‧‧‧Client rating data integration exchange module

220‧‧‧ storage unit

221‧‧‧Client rating data

222‧‧‧Server log

230‧‧‧Data Exchange Module

300‧‧‧Score and weighted analysis calculation server

310‧‧‧First Data Exchange Delivery Module

320‧‧‧weight distribution calculation module

330‧‧‧ storage unit

331‧‧‧ Personalized Relationship Factor Weight Table

332‧‧‧ video score results

333‧‧‧Video Sorting Recommended

334‧‧‧Server log

340‧‧‧weighted score calculation module

350‧‧‧Second Data Exchange Delivery Module

360‧‧‧Video Interpretation Data Access Interface

400‧‧‧Client device

401‧‧‧ input unit

402‧‧‧Client Scoring Module

403‧‧‧Voice (VOD) Video Score Module

404‧‧‧TV channel audio and video scoring module

405‧‧‧Data exchange interface

406‧‧‧Client display module

1 is an architectural diagram of a personalized weighted information system for personalized video and audio data scoring of the present invention; FIG. 2 is a structural diagram of an interpretation data collection server of the present invention; and FIG. 3 is a client score recording integration server of the present invention; FIG. 4 is a structural diagram of a scoring and weighting analysis computing server of the present invention; FIG. 5 is a flowchart of a personalized weighted information method for personalized video and audio data scoring of the present invention; FIG. 6 is a new flowchart of the present invention; A flow chart of the method of initial scoring operation when the video is on the shelf or the channel is premiered.

The other embodiments of the present invention will be readily understood by those skilled in the art from this disclosure.

It is to be understood that the structure, the proportions, the size, and the like of the present invention are intended to be used in conjunction with the disclosure of the specification, and are not intended to limit the invention. The conditions are limited, so it is not technically meaningful. Any modification of the structure, change of the proportional relationship or adjustment of the size should remain in this book without affecting the effects and the objectives that can be achieved by the present invention. The technical content disclosed in the invention can be covered. In the meantime, the terms "upper", "first", "second", "third" and "one" are used in this specification for convenience of description and are not intended to limit the invention. Implementable The change or adjustment of the relative relationship is considered to be within the scope of the invention.

The unit of the present invention may be a hardware structure, and the module of the present invention may be a code for running by a processor, such as a firmware or a software stored in a memory or a hard disk. Program.

The architecture diagram of the personalized video and audio data scoring integrated weighting information system of the present invention includes: the client device 400, comprising an input unit 401, configured to receive a user for an audio-visual And the video scoring data is transmitted to the client scoring module 402. The client scoring module 402 is configured to receive the user's audio and video score data for an audio and video through the input unit 401, and distinguish the user. The type of video and audio that is targeted is Video on Demand (VOD) video or TV channel audio and video; on-demand video score module 403, when the type of video and audio is selected audio and video, by on-demand (VOD) video score The module 403 processes the video score data; the TV channel video score module 404, when the type of the video is the TV channel video, the video channel score module 404 processes the video score data; the data exchange delivery interface 405, Accessing the video score data, the score and weight analysis calculation server 300 calculates the video score result 332 and the video sequence recommendation 333; a client display mode 406, the system contains on-demand video service area or the television channel of video information page, the page for displaying a plurality of information transfer audio and video data exchange interface 405 reading score results of 332 and AV 333 Sort recommended.

In an embodiment, the input unit 401 is an APP of a remote controller or a smart phone. The client display module 406 is for displaying on a display The software interface on the device, such as the service interface of the audio-visual service area or the information page of the television channel audio and video, for the user to watch and use.

The interpretation data collection server 100 is connected to a server of an external plurality of institutions to obtain score data of a plurality of institutions for audio and video, to standardize and convert the score data of the video to the rating data required by the system, and then normalize the score data. The scores of the same audio and video of the various institutions are integrated into the audio and video interpretation data 141.

In this embodiment, the so-called server of the external plurality of institutions refers to an existing film review or film scoring institution, such as an Internet movie database (IMDb), and a more favored viewer's preference for Rotten Potato (Rotten Tomato Index). , the mainland China users are more resident of the Dou Office website and Taiwanese users love Yahoo! Chimo movies and the like, but the invention is not limited thereto.

The client score record integration server 200 is configured to receive the video score data of the client device 400 to integrate the user profile and the video score data into the client rating data 221 .

The scoring and weighting analysis computing server 300 is configured to receive the client rating data 221 and the video interpretation data 141 to calculate and obtain the audio and video corresponding to the video through a relationship factor difference weighting analysis algorithm corresponding to the type of the relationship factor. The score result 332 and the video sequence recommendation 333 are provided, and the client display module 406 reads the video score result 332 and the video sequence recommendation 333 to display the video score result 332 and the video sequence recommendation 333.

In an embodiment, the client device 400 and the interpretation data collection server 100, the client score record integration server 200 or the score and the addition The weight analysis calculates the connections between the servers 300 and can be connected to each other via a wired network or a wireless network such as WiFi, 3G/4G, Bluetooth, or the like.

In an embodiment, when the video score data is processed by the on-demand video score module 403, the on-demand video score module 403 determines whether the user has input the video score data for the video, and if not, the on-demand video The scoring module uses the average of the scoring data of the audio and video by the plurality of institutions as the video score data of the user for the video.

In an embodiment, when the video channel score data is processed by the television channel video score module 404, the time point of the video score data input by the user for the video is corresponding to the time range of the channel program list or the movie playing time. The corresponding channel program or movie is used as the video score data of the video.

Please refer to FIG. 2 , which is a block diagram of the interpretation data collection server 100 in FIG. 1 . The interpretation data collection server 100 includes an organization rating data acquisition module 110 and a data conversion standardization module. The group 120, the data integration pairing module 130, the storage unit 140, and the storage unit access interface 150.

In this embodiment, the mechanism scoring data acquisition module 110 periodically connects the servers of the external organizations to obtain the scoring data of the audio and video of each institution, and the so-called periodic system, for example, once a day, once an hour, etc., automatically performs scheduling. .

The data conversion standardization module 120 is configured to standardize and convert the score data of the video and audio into the rating data required by the system, for example, re-arrange the rating data of different formats of the audio and video to the same system as the system. Rating data for the format.

The data integration matching module 130 is configured to receive the standardized score data to integrate the standardized score data of the same audio and video of the organizations into the audio and video interpretation data 141.

The storage unit 140 is configured to store the video interpretation data 141 and the server log 142, wherein the server log 142 is a history log for recording and standardizing the institutions. In addition, the storage unit access interface 150 provides access to the video interpretation material 141 by other servers, modules or units in the system.

Referring to FIG. 3, FIG. 3 is a structural diagram of the client rating record integration server 200 in FIG. 1. The client rating record integration server 200 includes: a client rating data integration switching module 210, The storage unit 220 and the data exchange delivery module 230.

The client rating data integration switching module 210 is configured to receive the video rating data of the client device 400 to integrate the user data and the input video score data into the client rating data 221 .

The storage unit 220 is configured to store the client rating data 221 and the server log 222, wherein the server log 222 is stored as a record of exchanging data with the client device 400 and an access record of the client rating data 221; The exchange delivery module 230 provides access to the client rating data 221 by other servers, modules or units in the system.

Referring to FIG. 4, FIG. 4 is a structural diagram of the scoring and weighting analysis computing server 300 in FIG. 1. The scoring and weighting analysis computing server 300 includes: a first data exchange delivery module 310, Weight distribution The calculation module 320, the storage unit 330, the weighted score calculation module 340, the second data exchange delivery module 350, and the video interpretation data access interface 360.

The first data exchange delivery module 310 exchanges data with the data exchange delivery module 230 in the client score record integration server 200 to obtain the client rating data 221; the audio and video interpretation data access interface 360, and the interpretation data. The storage unit storage interface 150 in the collection server 100 exchanges data to obtain the video interpretation material 141.

The weight distribution calculation module 320 calculates the relationship score difference weighting analysis algorithm for the type of the relationship factor by the client rating data 221 and the video interpretation data 141 to obtain a personalized relationship factor weight table 331, the relationship factor The category is one of the type, rating, director, actor, language, year, and place of the video or a combination thereof. The weighted score calculation module 340 calculates the weighted score of the personalized relationship factor weight table 331 and the video interpretation data 141 to obtain a video score result 332. The weighted score calculation module 340 sorts the video score results 332 of the respective audio and video. To produce the video sorting recommendation 333.

The storage unit 330 is configured to store the client weight data 331, the video score result 332, the video sequence recommendation 333, and the server log 334, wherein the server log 334 records the client weight data 331 and the video score result. 332 and the record that the video sequence recommendation 333 is accessed; the second data exchange delivery module 350 provides the client device 400 to read the video score result 332 and the video sequence recommendation 333.

In an embodiment, the score and weight analysis calculation server 300 does not establish a personalized relationship factor weight table 331, and the user completes the video for the first time. When the score is used, the relationship factor difference weighting analysis algorithm used by the weight distribution calculation module 320 first calculates the score difference between the client rating data 221 and the video interpretation data 141, according to the scores of the plurality of institutions. The difference value of the score difference is given by the degree of difference of each of the plurality of institutions, and the difference weight of each of the plurality of institutions corresponding to the client rating data 221 is calculated according to the degree of difference of the plurality of institutions. And the difference weight corresponding to the plurality of institutions outputs a personalized relationship factor weight table 331 according to the type of the relationship factor.

In an embodiment, the score and weight analysis calculation server 300 has established a personalized relationship factor weight table 331, and when the user completes the video score again, the weight distribution calculation module 320 uses the relationship factor difference weighting analysis algorithm to calculate The difference weight corresponding to the new plurality of institutions is obtained, and the difference weight output corresponding to the plurality of institutions is updated to the personalized relationship factor weight table 331 to generate a new personalized relationship factor weight table 331.

In an embodiment, the score and weight analysis calculation server 300 has established a personalized relationship factor weight table 331, and when the user does not view the video and audio and does not score the unviewed video, the unobserved video is a new video or an old video. In any case, the weighted score calculation module 340 calculates a score based on the personalized relationship factor weight table 331 and the new video interpretation data 141 when the interpretation data collection server 100 generates a new video interpretation data 141. A new video score result 332 is generated.

In one embodiment, the storage unit 140, 220, 330 may be a hard disk, a floppy disk, a flash drive, or a compact disk, but the invention is not limited thereto.

Please refer to FIG. 5 and FIG. 6 , which is a method for comprehensive weighting information of personalized audio and video data scores according to the present invention, which may be a personalized weighting method S100 for personalized audio and video data in FIG. 5 , or by FIG. 5 . The personalized video and audio data scoring integrated weighting method S100 and the method S200 of the audio and video scoring operation of FIG. 6 are composed.

Specifically, a personalized video and audio material score comprehensive weighting method S100 includes:

(S110): Obtaining the user's audio and video score data for a video and audio, wherein the input unit 401 of the client device 400 receives the video score data of the user for a video.

(S111): distinguishing the type of video and audio of the video rating information, wherein the type of the video for which the user is directed by the client scoring module 402 is an audio-visual or television channel video.

(S121): On-demand (VOD) video score, wherein the video score data is processed by the on-demand video score module 403 when the type of the video is the on-demand video.

(S122): TV channel video score, wherein the video score data is processed by the television channel video score module 404 when the type of the video is television channel video.

(S130): Whether to score, wherein the on-demand (VOD) video score unit 412 determines whether the user has performed a on-demand (VOD) video score. If there is a score, the process goes to step S142, and if there is no score, the process goes to step S141.

(S141): The subscription video is given a neutral score (average of other institutions). That is, the on-demand (VOD) video score module 403 determines the user Whether the audio and video score data is input to the video and audio. If not, the on-demand video score module 403 uses the average of the score data of the audio and video as the user's video score data for the video and audio. .

(S142): Whether you subscribe or not, you can score at each stage of the introduction page and playback.

(S143): corresponding to the channel program list, at each time point of the video playback, the television channel video score module 404 processes the time point of the video score data input by the user for the video to The channel program schedule or the time interval of the movie playing, so that the corresponding channel program or movie is used as the video score data of the video.

(S150): a relationship factor difference weighting analysis calculation is performed from the given score, wherein the score and weight analysis calculation server 300 reads the client rating data 221 and the interpretation data collection server 100 to the client rating record integration server 200. The video interpretation data 141 of the video is scored by a plurality of organizations, and the weighted interpretation calculation module 320 performs the relationship factor weighting analysis and calculation on the read video interpretation data 221 to obtain The difference weights corresponding to the plurality of institutions.

(S160): output a personalization relationship factor weight table, wherein the weight distribution calculation module 320 outputs the difference weight corresponding to the plurality of institutions as a humanized relationship factor weight table 331 according to the type of the relationship factor and stores the same in the storage. Within unit 330.

In the above (S150) step, the type of the relationship factor is one of the type, classification, director, actor, language, year, and place of the video or a combination thereof.

The step (S160) further includes the step of updating the personalized relationship factor weight table 331 when the new difference weight corresponding to the plurality of institutions is obtained.

In addition, the above-mentioned personalized video and audio data scoring integrated weighting method S100, the method S200 for further performing the audio and video scoring operation when a new video or audio channel is first broadcasted, includes the following steps:

(S210) When the new video is on the shelf or the channel video is premiered.

(S220) extracting the interpretation data of the new video and audio, wherein the weighted score calculation module 340 reads the new video or channel video premiere video interpretation material 141 from the memory unit access interface 360 through the video and audio interpretation data access interface 360.

(S230) The personalized relationship factor weights are retrieved, wherein the weighted score calculation module 340 retrieves the personalized relationship factor weight table 331 from the storage unit 330.

(S240) Calculating the personalized weighted score according to each institution score of the audiovisual interpretation data, wherein the weighted score calculation module 340 performs the score calculation on the personalized relationship factor weight table 331 and the video interpretation data 141 to obtain the video score result 332. .

(S250) A video sequence recommendation is generated, wherein the weighted score calculation module 340 sorts according to the video score result 332 to generate the video and audio sequence recommendation 333 of the video.

(S260) displaying a video score result and a video sequence recommendation, wherein the client display module 402 reads the video score result 332 and the video sequence recommendation 333 to the score and weight analysis calculation server, and displays the result in the client display module 402. Information page of the audio-visual service area or TV channel in.

The method S200 further includes performing a score calculation and generating a video sequence recommendation for the old video that is not viewed and unrated by the user, that is, the new video processing step in the method S200 is changed to the old video processing step.

In another embodiment of the method for personalizing video and audio data scoring integrated weighting information according to the present invention, when the scoring and weighting analysis computing server 300 has established the personalized relationship factor weighting table 331, and the user completes the video score again, the execution is performed. Steps S150, S160, and S200 calculate a personalized relationship factor weight table 331, a video score result 332, and a video sequence recommendation 333.

For example, the user gives a video score of 8.7 to an audiovisual, and the established personalized relationship factor weight table 331 is obtained by the user in the previous 9 video scores and is calculated through steps S150 to S160. The personalized relationship factor weight table 331 has been established as shown in Table 1 below.

A, B, C and D are the scoring institutions for the difference comparison, and a, b, c, d and e are the relationship factor types.

First, the relationship factor difference weighting analysis algorithm in step S150 is performed according to the video score data of the user, and the meanings of the symbols and variables in the algorithm are as follows:

G R ^K , n is the number of institutions.

Next, calculate the absolute value of the user's current score and the score difference ( Differential , D _u ) of the video.

D _u (M,O)=|R _u (M)-R _o (M)|,m is the number of R _u (M)

The video interpretation data access interface 360 of the scoring and weighting analysis computing server 300 reads the video interpretation data 141 of the rating of the video of the user rating to the storage unit access interface 150 in the interpretation data collection server 100. The video interpretation data is as follows: R _A ( b, d ) = 8.5, R _B ( a, b, d ) = 9.0, R _C ( a, b, d ) = 8.3, R _D ( a, b,d )=8.2

Therefore, the score difference between each institution and this user rating is: D _u ( M, A ) = 0.2, D _u ( M, B ) = 0.3, D _u ( M, C ) = 0.4, D _u ( M, D )=0.5

Then according to the absolute value of the above score difference, the score of the degree of difference is given: Then the degree of difference in each institution is as follows: P _A ( D _u )=7, P _B ( D _u )=7, P _C ( D _u )=5, P _D ( D _u )=5 and are calculated separately from the distribution The video is based on the difference weights of the agencies in this rating. Then the weight difference of each institution is as follows:

Finally, the final combined weights of each institution are calculated based on the user's audio and video scores for each relationship factor (type): Among them, the threshold can be set according to the demand to the user's rating.

After joining this rating, the newly generated personalization relationship factor weight table 331 is shown in Table 2 below:

The calculation of W _A (a) = 0.254 in the above personalized relationship factor weight table 331 is as follows:

Wherein the system is 0.25 W Table _A (a) value of 1, 9 of Table 1 Weights personal relationship table 331 times the weight of the previously calculated, _A system of the above-mentioned W (M) of the weight difference, the total number of calculations is 10 after the addition of this score.

When the difference in the weight table if the system has not been established, by the above-described difference weights W _A (M), W _B (M), W _propionate (M), W _D (M) to establish based on the relationship between factors that Weights personal relationships weight table 331.

After obtaining the personalized relationship factor weight table 331 corresponding to each institution according to the result of the above calculation method, the S200 method is executed when the video is newly added or the channel video is premiered in the future, and the weighted score calculation module 340 stores the data by video and audio. The interface 360 reads the video interpretation material 141 corresponding to the new video or channel video premiere to the storage unit access interface 150 in the interpretation data collection server 100, and the weighted score calculation module 340 reads the read video interpretation data 141. The score calculation is performed with the personalized relationship factor weight table 331 in Table 2 above, and the score calculation is as follows:

The process of generating personalized weighted scores for new audio-visual shelves or channel video premieres. If there is a piece (the film is classified as a , c , e type), the audio-visual interpretation data obtained by the four-institution score is as follows: R _A ( a, c, e ) = 6.5, R _B ( a, c, e ) = 7.6, R _C ( a, c, e ) = 8.2, R _D ( a, c, e ) = 8.0

Calculate the new video score result R _new (a, c, e) according to the institutional weight of the relationship factor contained in each video:

Finally, each new shelf or channel first broadcast video will have a score of newly calculated R _new ( M ), and the weighted score calculation module 340 sorts the new video score results, which is based on each use. The results are 333 recommended by the individuals and their relationship factors.

As can be seen from the above, the present invention is obtained by scoring and calculating the audio-visual interpretation data and the personalized relationship factor weighting table integrated by the scores of the audio-visual scores by a plurality of institutions, and obtaining the personalized video-audio score results for each of the audio-visual sounds, and The video score results are sorted to generate a personalized video sequence recommendation. Therefore, the present invention solves the problem that the user can only make a selection reference through the score of a single institution, and can also present himself and different scores. The difference between the taste and type of the organization, and then the best sorting recommendation.

The above embodiments are intended to illustrate the principles of the invention and its effects, and are not intended to limit the invention. Anyone who is familiar with this skill can The above embodiments are modified without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention should be as set forth in the appended claims.

Claims (10)

A system for personalizing video and audio data scoring integrated weighting information includes: a client device, comprising a client scoring module and a client display module, wherein the client scoring module is configured to receive a user for an audiovisual Video score data; the client score record integration server is configured to receive the video score data of the client device, to integrate the user data and the video score data into client rating data; and to interpret the data collection server And a system for connecting the plurality of institutions to obtain the score information of the plurality of institutions for integrating the audio and video scores into the audio and video interpretation data; and the scoring and weighting analysis calculation server for receiving the client The score data and the audiovisual interpretation data are obtained by using a relationship factor difference weighted analysis algorithm corresponding to the type of the relationship factor to obtain a video score result and a video sequence recommendation corresponding to the audio and video, and providing the client display module to read the data. Video score results and video sorting recommendations to display the video score results and video sorting push . The system of claim 1, wherein the scoring and weighting analysis calculation server further comprises: a weight distribution calculation module, wherein the relationship factor difference weighting analysis algorithm is used to calculate and obtain the corresponding number of institutions a weighting difference, wherein the difference weight corresponding to the plurality of institutions outputs a personalization factor weight table according to the type of the relationship factor; The weighted score calculation module is configured to perform the score calculation on the personalized relationship factor weight and the video interpretation data to obtain the video score result, and then sort the video score result to generate the video sound sequence recommendation. The system of claim 2, wherein the client scoring module is further configured to distinguish that the type of the video for which the user is directed is an audio-visual or television channel, and the client device further includes An input unit, configured to receive the video rating data, to transmit the video rating data to the client rating module; and the optional video rating module, when the type of the audio and video is selected audio and video, determine the user Whether the video score data is input to the video and audio recording, if not, the on-demand video score module uses the average of the rating data of the audio and video as the user's rating data for the video and audio; a TV channel video score module, when the type of the video is a TV channel video, the time point of the audio score data input by the user for the video is corresponding to the time range of the channel program table or the movie playing time, Corresponding channel program or movie as the video score data of the audio and video; and a data exchange delivery interface for accessing the video score data, the video and audio The result of the rating and the recommended order of the video. The system of claim 2, wherein the weight distribution calculation module is configured to update the personalized relationship factor weight table when the new difference weight corresponding to the plurality of institutions is obtained. The system of claim 2, wherein the weighting analysis algorithm used by the weighting calculation module first calculates the score between the client rating data and the audiovisual interpretation data. a score difference of a plurality of institutions, and a score of the degree of difference of each of the plurality of institutions is given according to an absolute value of the score difference, and the plural score corresponding to the score of the client is calculated according to the degree of the degree of difference of each of the plurality of institutions The difference weight of the institutions. The system of claim 2, wherein the weighted score calculation module generates a new audio and video interpretation data according to the personalized relationship factor weighting table and the new video interpretation The data is scored to generate new video score results. A method for personalizing video and audio data to score comprehensive weighted information, comprising: obtaining a video score data of a user for an audio and video, and integrating the user data and the video score data into a client rating data; The data and a video interpretation data integrated by the plurality of institutions of the audiovisual score data are obtained by a correlation factor difference weighted analysis algorithm corresponding to the type of the relationship factor, and the difference weights corresponding to the plurality of institutions are obtained; The difference weight corresponding to the plurality of institutions is output as a humanized relationship factor weight table according to the type of the relationship factor. The method of updating the personalized relationship factor weighting table when the method of claim 7 further includes obtaining the new difference weight corresponding to the plurality of institutions. The method of claim 7, wherein the relationship factor difference weighting analysis algorithm further comprises the following calculating step: calculating a score difference between the client rating data and the plurality of institutions between the video interpretation data And determining, according to the absolute value of the score difference, a degree of difference distribution of each of the plurality of institutions; and calculating, according to the degree of difference of the plurality of institutions, a difference weight of each of the plurality of institutions corresponding to the client rating data. The method of claim 7, wherein after obtaining the personalized relationship factor weight table, the method further comprises the following steps: calculating the personalized relationship factor weight table and the video interpretation data to generate audio and video The result of the scoring; and sorting according to the result of the video scoring to generate the audio and video ranking recommendation of the video.