CN106658028A - Clustering processing method of multi-server video on demand resources - Google Patents
Clustering processing method of multi-server video on demand resources Download PDFInfo
- Publication number
- CN106658028A CN106658028A CN201611262121.9A CN201611262121A CN106658028A CN 106658028 A CN106658028 A CN 106658028A CN 201611262121 A CN201611262121 A CN 201611262121A CN 106658028 A CN106658028 A CN 106658028A
- Authority
- CN
- China
- Prior art keywords
- video
- client
- resource
- processing method
- clustering processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/21—Server components or server architectures
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs
- H04N21/2343—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/236—Assembling of a multiplex stream, e.g. transport stream, by combining a video stream with other content or additional data, e.g. inserting a URL [Uniform Resource Locator] into a video stream, multiplexing software data into a video stream; Remultiplexing of multiplex streams; Insertion of stuffing bits into the multiplex stream, e.g. to obtain a constant bit-rate; Assembling of a packetised elementary stream
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/238—Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
- H04N21/2387—Stream processing in response to a playback request from an end-user, e.g. for trick-play
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The invention relates to a clustering processing method of multi-server video on demand resources. The method at least comprises the following steps: when a client sends a video on demand request, obtaining media playing data of a log database of the client, and performing preprocessing to obtain a bandwidth code rate and a supported media format of media playing of the client; performing clustering analysis on the requested resources in combination with the media format of the media playing of the client, and using an obtained clustering set as candidate resources, marking weight coefficient as omega; in combination with the bandwidth code rate of the media playing of the client, performing clustering analysis on the server where a certain resource is located, and marking the weight coefficient as phi; multiplying the two weight coefficients, and marking a sequence of numerical values from large to small as final recommendation values from high to low, and sending the final recommendation values to the client for display. A code rate file suitable for playing is matched for a user through the clustering processing method, instant video on demand is realized, and the efficiency of video on demand is improved.
Description
Technical field
The present invention relates to Technology of Mobile Multimedia field, and in particular at a kind of cluster of multiserver video request program resource
Reason method.
Background technology
Video request program, for according to the VOD system of the requirement displaying video programs of spectators；It can make spectators straight at any time
Contact broadcasts the program for wishing to watch, it is possible to adjust progress, speed of broadcasting etc. at any time.The time is expended for conventional Video on Demand
A kind of long problem, clustering processing method of multiserver video request program resource disclosed in patent application 201110072552X is led to
Cross server and video are analyzed according to certain analysis of statistical data result simultaneously, the resource of video on-demand system is entered
Row cluster, the process to order request provides guiding so that program request can comparatively fast be accomplished, and preferably avoids obstruction.
Because current video on demand techniques supports that side is broadcast below, when the code check of video resource is mismatched with consumer wideband,
Above-mentioned clustering algorithm program request speed in practice is very smooth still without reaching；As video file code check is more than consumer wideband,
It is easily caused video file interruption.Additionally, according to above-mentioned clustering method, it is impossible to which selection is broadcast with the video that user's players match
Form is put, leads to not realize instant program request.
The content of the invention
It is an object of the invention to overcome defect of the prior art, a kind of the poly- of multiserver video request program resource is designed
Class processing method, is the suitable code check file played of user's matching by the clustering processing method, realizes instant program request, improves point
Broadcast efficiency.
For achieving the above object, the technical solution adopted in the present invention is a kind of cluster of multiserver video request program resource
Processing method, methods described at least comprises the steps：
S1, when client sends order request, the media play data of the log database of client are obtained, through pre-
After process, the bandwidth code check of client media broadcasting and the media formats of support are obtained；
S2, the media formats with reference to client media broadcasting carry out cluster analysis to request resource, the cluster set for drawing
Used as candidate resource, its weight coefficient is labeled as Ω；
S3, the bandwidth code check with reference to client media broadcasting carry out cluster point to some resource place server therein
Analysis, its weight coefficient is labeled as Π；
S4, two kinds of weight coefficients are multiplied, numerical value sequence from big to small is demarcated as into finally pushing away from high to low
Value is recommended, is sent to client and is shown.
In a preferred scheme, the cluster analysis carried out in step S2 is entered according to each attribute of vod server
Row is weighed；The attribute includes：Credit rating λ, code check matching degree d, stock number ε, stabilityIdle degree β and ease for use α；Its
In, in unit interval when code check matching degree d represents program request, the volume of transmitted data between vod server and external network
And the matching degree of the volume of transmitted data between client and external network, d is bigger, and matching degree is less.
Further, in the unit interval in program request, the volume of transmitted data between vod server and external network is returned
With τ after one changeb1Represent, with τ after the volume of transmitted data normalization between client and external networkb2Represent, by calculating both
Apart from d=(τb1, τb2), d is bigger, and matching degree is less.
On the basis of such scheme, Π is expressed as described in algorithm
Wherein, credit rating is λ=λv/λt, significant response number of times is λv, global response number of times is λt。
In presently preferred scheme, the cluster analysis carried out in step S3 is each attribute according to video resource
Weighed；The attribute includes：Using counting δ, ease for use α, stability φ, credit rating λ and video format coupling number τ；
Wherein, video format coupling number τ represents the video format of video resource and the match condition of client media broadcast format, with
With whether being calculated；Value is bigger, and matching degree is higher.
Specifically, the video format of video resource is matched with client media broadcast format, and τ values are 1；Video resource is regarded
Frequency form is mismatched with client media broadcast format, and τ values are demarcated according to transcoding time limit interval during program request, and calibration value is
0.1-0.9；Specifically, it is [μ to transcoding time limit interval division1, μ2), [μ3, μ4)...[μ2*n-1, μ2*n] n classes, calibration value according to
Numerical value minizone to the big interval of numerical value is demarcated from large to small successively, that is, be worth bigger, and the transcoding time limit is shorter.
Further, the Ω is expressed as：
Wherein, credit rating is λ=λv/λt, significant response number of times is λv, global response number of times is λt。
The present invention in a preferred version, the attribute also includes value of utility κ, and the value of utility κ is according to video
Resource is calculated by the preference of each user, and κ values are bigger, and utility is better.On this basis, the Ω is expressed as：
Wherein, credit rating is λ=λv/λt, significant response number of times is λv, global response number of times is λt。
The clustering processing method of multiserver video request program resource of the present invention is by the multiserver to video on-demand system
And its multiple video files carry out cluster analysis, weighed according to multiple attributes of vod server and video resource, to program request system
The request of system provides guiding so as to which program request can be completed fast and accurately, improves VOD system performance.
When client selects program request video file, in attribute multiserver is weighed by addition code check matching degree, choosing
Select the server with Optimum Matching degree；And the video format coupling number for passing through video file is preferred, shorten the transcoding time limit,
So it is avoided that side during broadcasting below because selecting files form and the not high problem of code check matching degree causes to play and interrupts.
Description of the drawings
Fig. 1 is method flow diagram provided in an embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings and examples, the specific embodiment of the present invention is further described.Following examples are only
For clearly illustrating technical scheme, and can not be limited the scope of the invention with this.
A kind of clustering processing method of multiserver video request program resource of the present invention, shown in Fig. 1, methods described is at least wrapped
Include following step：
S1, when client sends order request, the media play data of the log database of client are obtained, through pre-
After process, the bandwidth code check of client media broadcasting and the media formats of support are obtained；
S2, the media formats with reference to client media broadcasting carry out cluster analysis to request resource, the cluster set for drawing
Used as candidate resource, its weight coefficient is labeled as Ω；
S3, the bandwidth code check with reference to client media broadcasting carry out cluster point to some resource place server therein
Analysis, its weight coefficient is labeled as Π；
S4, two kinds of weight coefficients are multiplied, numerical value sequence from big to small is demarcated as into finally pushing away from high to low
Value is recommended, is sent to client and is shown.
In step sl, noise data, the bandwidth to media play in the client unit interval are specially removed in pretreatment
Code check is normalized.It is to remove noise data to the purpose of daily record data pretreatment, it is impossible to be used as cluster point
The User operation log of analysis data is deleted.First the bandwidth code check numerical value of client is extracted by setting keyword patterns；So
Afterwards deletion noise data is carried out by given threshold, the noise data is mostly that abnormal code check becomes in copic viewing system
Change, can be eliminated by given threshold.
The cluster analysis carried out in step S2 is weighed according to each attribute of vod server；The attribute bag
Include：Credit rating λ, code check matching degree d, stock number ε, stabilityIdle degree β and ease for use α.
Credit rating λ：The significant response proportion to order request is referred to, to respond video on-demand request and start to transmit video
Data are used as a significant response.The significant response number of times of definition is λv, global response number of times is λt, credit rating is λ=λv/λt,
It is worth bigger, credit rating is higher.
Code check matching degree d：In unit interval when representing program request, the data transfer between vod server and external network
The matching degree of amount and the volume of transmitted data between client and external network, d is bigger, and matching degree is less.In program request
In unit interval, with τ after the volume of transmitted data normalization between vod server and external networkb1Expression, client and outside
With τ after volume of transmitted data normalization between networkb2Represent, by calculating both apart from d=(τb1, τb2), d is bigger, matching degree
It is less.Before the method is calculated, multiple numerical value and τ of d are presetb1And τb2The interval correspondence of difference, that is, deck watch is set；τb1With
τb2Difference it is bigger, d is less, conversely, then bigger.In calculating process, directly by τb1And τb2Difference compare the table, find out
Corresponding d values.The property value is calculated according to daily record data, it is therefore desirable to which setting updates the time limit, again according to the day for updating
Will data are calculated d values.
Stock number ε：For the relative quiescent value of server, the video resource quantity that server possesses, the ε after normalization are represented
Represent；Value is bigger, and stock number is more.
StabilityStability and fluency to server video playback after program request success, with average frame losing number of times
Weigh, use after normalizationValue is less, and stability is better with fluency.
Idle degree β：The frequent degree of server process order request is represented, is represented with β after normalization；Value is less, service
It is bigger that device can process request ability.
Ease for use α：To the process time length asked, represented with α after normalization；Value is less, and ease for use is higher.
Attribute in sum, the Π is expressed as
Interval division is carried out to evaluation result, server is divided into n classes；Calculated Π numerical value is alternatively optimum
One criterion of server.Every property value dynamic calculation, dynamic updates weight coefficient.
The cluster analysis carried out in step S3 is weighed according to each attribute of video resource；The attribute includes：
Using counting δ, ease for use α, stability φ, credit rating λ and video format coupling number τ.
Using counting δ：Sum of the program request to resource successful request, is represented after normalization with δ；Value is bigger, represents that resource makes
With more frequent.
Ease for use α：Process time length to resource successful request, is represented after normalization with α, and value is less, and ease for use is got over
It is high.
Stability φ：The stability played in program request success rear video to server and fluency, are weighed with frame losing number of times,
Represented with φ after normalization, value is less, and stability is better with fluency.
Credit rating λ：Significant response proportion to resource order request, to respond video on-demand request and start to transmit video
Data are used as a significant response.The significant response number of times of definition is λv, global response number of times is λt, credit rating is λ=λv/λt,
It is worth bigger, credit rating is higher.
Video format coupling number τ：The video format of video resource and the match condition of client media broadcast format are represented,
Calculated with whether matching；Value is bigger, and matching degree is higher.The video format of video resource and client media broadcast format
Match somebody with somebody, τ values are 1；The video format of video resource is mismatched with client media broadcast format, when τ values are according to transcoding during program request
Limit interval is demarcated, and calibration value is 0.1-0.9；Specifically, it is [μ to transcoding time limit interval division1, μ2), [μ3, μ4)...
[μ2*n-1, μ2*n] n classes, calibration value demarcated from large to small successively according to numerical value minizone to the big interval of numerical value, that is, be worth it is bigger,
The transcoding time limit is shorter.
For the cluster analysis of resource：The Ω is expressed as：
Interval division is carried out to evaluation result, server is divided into n classes；Calculated Π numerical value is alternatively optimum
One criterion of server.Every property value dynamic calculation, dynamic updates weight coefficient.
In another embodiment, the attribute also includes value of utility κ, and the value of utility κ is according to video resource by each
The preference of individual user is calculated, in the embodiment, total program request of the VOD system according to each video resource within the setting time limit
Time length evaluates the preference of user；The real time length of the video is watched as t with userv, user's video-on-demand times with
Video total time, the product of length was t, and preference is calculated as κ=tv/t.When video resource is hot broadcast resource, κ values are bigger,
Utility is better.
The Ω is expressed as：
During specific embodiment, according to the weight coefficient of calculated each classification, consequently recommended knot must be obtained after multiplication
Really, consequently recommended result is arranged according to descending order, the video recommendations of TOP-N, are transmitted to client before selecting
Show, select for user.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, on the premise of without departing from the technology of the present invention principle, some improvements and modifications can also be made, these improvements and modifications
Also should be regarded as protection scope of the present invention.
Claims (10)
1. a kind of clustering processing method of multiserver video request program resource, methods described at least comprises the steps：
S1, when client sends order request, obtain client log database media play data, through pretreatment
Afterwards, the bandwidth code check for obtaining client media broadcasting and the media formats supported；
S2, the media formats with reference to client media broadcasting carry out cluster analysis to request resource, and the cluster set cooperation for drawing is
Candidate resource, its weight coefficient is labeled as Ω；
S3, the bandwidth code check played with reference to client media carry out cluster analysis to some resource place server therein,
Its weight coefficient is labeled as Π；
S4, two kinds of weight coefficients are multiplied, the consequently recommended value that numerical value sequence from big to small is demarcated as from high to low,
Send to client and shown.
2. the clustering processing method of multiserver video request program resource as claimed in claim 1, it is characterised in that in step S2
The cluster analysis for carrying out is weighed according to each attribute of vod server；The attribute includes：Credit rating λ, code check
With degree d, stock number ε, stabilityIdle degree β and ease for use α；
Wherein, in unit interval when code check matching degree d represents program request, the data between vod server and external network
The matching degree of the volume of transmitted data between transmission quantity and client and external network, d is bigger, and matching degree is less.
3. the clustering processing method of multiserver video request program resource as claimed in claim 2, it is characterised in that in program request
Unit interval in, with τ after the normalization of volume of transmitted data between vod server and external networkb1Represent, client with it is outer
With τ after volume of transmitted data normalization between portion's networkb2Represent, by calculating both apart from d=(τb1, τb2), d is bigger, matching
Degree is less.
4. the clustering processing method of the multiserver video request program resource as any one of claim 2-3, its feature exists
In the Π is expressed as
Wherein, credit rating is λ=λv/λt, significant response number of times is λv, global response number of times is λt。
5. the clustering processing method of multiserver video request program resource as claimed in claim 1, it is characterised in that in step S3
The cluster analysis for carrying out is weighed according to each attribute of video resource；The attribute includes：Using counting δ, ease for use
α, stability φ, credit rating λ and video format coupling number τ；
Wherein, video format coupling number τ represents the video format of video resource and the match condition of client media broadcast format,
Calculated with whether matching；Value is bigger, and matching degree is higher.
6. the clustering processing method of multiserver video request program resource as claimed in claim 4, it is characterised in that video resource
Video format match with client media broadcast format, τ values be 1；The video format of video resource is played with client media
Format mismatching, τ values are demarcated according to transcoding time limit interval during program request, and calibration value is 0.1-0.9；Specifically, to transcoding
Time limit interval division is [μ1, μ2), [μ3, μ4)...[μ2*n-1, μ2*n] n classes, calibration value is according to numerical value minizone to the big interval of numerical value
Demarcated from large to small successively, that is, be worth bigger, the transcoding time limit is shorter.
7. the clustering processing method of multiserver video request program resource as claimed in claim 6, it is characterised in that the Ω tables
It is shown as：
Wherein, credit rating is λ=λv/λt, significant response number of times is λv, global response number of times is λt。
8. the clustering processing method of multiserver video request program resource as claimed in claim 5, it is characterised in that the attribute
Value of utility κ is also included, the value of utility κ is calculated according to video resource by the preference of each user, and κ values are bigger, effectiveness
Property is better.
9. the clustering processing method of multiserver video request program resource as claimed in claim 8, it is characterised in that the Ω tables
It is shown as：
Wherein, credit rating is λ=λv/λt, significant response number of times is λv, global response number of times is λt。
10. the clustering processing method of multiserver video request program resource as claimed in claim 1, it is characterised in that step S1
In pretreatment specially remove noise data, place is normalized to the bandwidth code check of media play in the client unit interval
Reason.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611262121.9A CN106658028A (en) | 2016-12-30 | 2016-12-30 | Clustering processing method of multi-server video on demand resources |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611262121.9A CN106658028A (en) | 2016-12-30 | 2016-12-30 | Clustering processing method of multi-server video on demand resources |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106658028A true CN106658028A (en) | 2017-05-10 |
Family
ID=58838881
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611262121.9A Pending CN106658028A (en) | 2016-12-30 | 2016-12-30 | Clustering processing method of multi-server video on demand resources |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106658028A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111479139A (en) * | 2019-01-23 | 2020-07-31 | 上海哔哩哔哩科技有限公司 | Video playing control method and device based on video source selection and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101246504A (en) * | 2008-03-31 | 2008-08-20 | 北京搜狗科技发展有限公司 | Clustering method, device and system |
| CN101419614A (en) * | 2008-12-03 | 2009-04-29 | 深圳市迅雷网络技术有限公司 | Video resource clustering method and device |
| CN102196297A (en) * | 2011-03-24 | 2011-09-21 | 东莞中山大学研究院 | Cluster processing method for multiserver VOD (video on demand) resource |
| CN103379362A (en) * | 2012-04-24 | 2013-10-30 | 腾讯科技（深圳）有限公司 | Video-on-demand method and system |
| CN105163181A (en) * | 2015-08-05 | 2015-12-16 | 中国科学院声学研究所 | Online video program classification method and device thereof |
| CN105744357A (en) * | 2016-02-29 | 2016-07-06 | 哈尔滨超凡视觉科技有限公司 | Method for reducing network video bandwidth occupation based on online resolution improvement |
| CN105812834A (en) * | 2016-05-10 | 2016-07-27 | 南京大学 | Video recommendation server, recommendation method and pre-caching method based on cluster information |
-
2016
- 2016-12-30 CN CN201611262121.9A patent/CN106658028A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101246504A (en) * | 2008-03-31 | 2008-08-20 | 北京搜狗科技发展有限公司 | Clustering method, device and system |
| CN101419614A (en) * | 2008-12-03 | 2009-04-29 | 深圳市迅雷网络技术有限公司 | Video resource clustering method and device |
| CN102196297A (en) * | 2011-03-24 | 2011-09-21 | 东莞中山大学研究院 | Cluster processing method for multiserver VOD (video on demand) resource |
| CN103379362A (en) * | 2012-04-24 | 2013-10-30 | 腾讯科技（深圳）有限公司 | Video-on-demand method and system |
| CN105163181A (en) * | 2015-08-05 | 2015-12-16 | 中国科学院声学研究所 | Online video program classification method and device thereof |
| CN105744357A (en) * | 2016-02-29 | 2016-07-06 | 哈尔滨超凡视觉科技有限公司 | Method for reducing network video bandwidth occupation based on online resolution improvement |
| CN105812834A (en) * | 2016-05-10 | 2016-07-27 | 南京大学 | Video recommendation server, recommendation method and pre-caching method based on cluster information |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111479139A (en) * | 2019-01-23 | 2020-07-31 | 上海哔哩哔哩科技有限公司 | Video playing control method and device based on video source selection and storage medium |
| US11671666B2 (en) | 2019-01-23 | 2023-06-06 | Shanghai Bilibili Technology Co., Ltd. | Video playing control method, device and storage medium based on a video source selection |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11490145B2 (en) | Content insertion in streaming media content | |
| US10051341B2 (en) | Supplementing live broadcast with relevant information streams | |
| US9558222B1 (en) | Blended ranking of dissimilar populations using an N-furcated normalization technique | |
| US9396258B2 (en) | Recommending video programs | |
| US9218051B1 (en) | Visual presentation of video usage statistics | |
| US8583791B2 (en) | Maintaining a minimum level of real time media recommendations in the absence of online friends | |
| CN107249145B (en) | A kind of method of pushing video | |
| US20120158527A1 (en) | Systems, Methods and/or Computer Readable Storage Media Facilitating Aggregation and/or Personalized Sequencing of News Video Content | |
| US20120317123A1 (en) | Systems and methods for providing media recommendations | |
| US20120304224A1 (en) | Mechanism for Embedding Metadata in Video and Broadcast Television | |
| US7849092B2 (en) | System and method for identifying similar media objects | |
| US8248940B2 (en) | Method and apparatus for targeted content delivery based on internet video traffic analysis | |
| WO2015165396A1 (en) | Method and system for playing website video, and website server | |
| CA2610038A1 (en) | Providing community-based media item ratings to users | |
| WO2003067467A1 (en) | Fast hash-based multimedia object metadata retrieval | |
| KR20180053673A (en) | Clustering of viewing time for video search | |
| CN104135678A (en) | Method for realizing video advertisement putting in video on demand based on HLS protocol | |
| CN102056018A (en) | Method and system for providing TV guide and method for providing program-requesting information | |
| US9607084B2 (en) | Assigning a single master identifier to all related content assets | |
| CN106658028A (en) | Clustering processing method of multi-server video on demand resources | |
| US11151586B2 (en) | Methods and apparatus to identify non-traditional asset-bundles for purchasing groups using social media | |
| EP2504802A2 (en) | Method and apparatus for selecting content | |
| US8745056B1 (en) | Spam detection for user-generated multimedia items based on concept clustering | |
| CN114173200B (en) | Video management pushing method and device based on private radio and television network | |
| KR102475018B1 (en) | Interactive video system that supports immediate search of product information in indirect advertising |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170510 |