CN109168044B - Method and device for determining video characteristics - Google Patents
Method and device for determining video characteristics Download PDFInfo
- Publication number
- CN109168044B CN109168044B CN201811183929.7A CN201811183929A CN109168044B CN 109168044 B CN109168044 B CN 109168044B CN 201811183929 A CN201811183929 A CN 201811183929A CN 109168044 B CN109168044 B CN 109168044B
- Authority
- CN
- China
- Prior art keywords
- video
- user
- target
- users
- preset
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012216 screening Methods 0.000 claims abstract description 24
- 238000004891 communication Methods 0.000 claims abstract description 16
- 230000006399 behavior Effects 0.000 claims description 50
- 238000004590 computer program Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
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
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/251—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/252—Processing of multiple end-users' preferences to derive collaborative data
-
- 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]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
-
- 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]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Computing Systems (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
- Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
Abstract
The embodiment of the invention provides a method and a device for determining video characteristics, relates to the technical field of communication, and aims to solve the problem that the association degree of the video characteristics determined by the prior art and a user is low. The scheme of the embodiment of the invention comprises the following steps: screening out a target user group from users of the video website, wherein the target user group comprises users of which the association degree with a preset dimension reaches a preset threshold value, the preset dimension is a category to which the target video belongs, and then determining the characteristics of the target video according to behavior information of each user in the target user group on the target video.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for determining video characteristics.
Background
At present, video software can generally analyze video characteristics according to user behaviors, and then measure the quality of a video according to the video characteristics, or recommend the video to a user according to the video characteristics. The video features may reflect the behavior of the user on the video, for example, the video features may be the click rate of all users using the video software on a certain video, and to measure the quality of a movie, the video software may count the click rate of all users in the video software on the movie, and then calculate the click rate according to the click rate, and determine that the quality of the movie with the high click rate is higher.
However, there may be a portion of users who click only by being attracted by a movie poster or a title, and are not actually interested in movie content, so that video features (such as the click rate of the video) determined according to the behaviors of the portion of users are less correlated with the interests of the users, and the movie quality determined according to the video features is not accurate enough.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for determining video characteristics, so as to solve the problem that the relevance between the video characteristics determined by the prior art and the interest of a user is low. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for determining video features, including:
screening a target user group from users of a video website, wherein the target user group comprises users of which the association degree with a preset dimension reaches a preset threshold value, and the preset dimension is a category to which a target video belongs;
and determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video.
In one possible implementation, before the screening out the target user group from the users of the video website, the method further includes:
and setting the preset dimension and the preset value, wherein the user with the correlation degree with the preset dimension reaching the preset threshold value is the user with the score of the preset dimension reaching the preset threshold value.
In one possible implementation, the screening out a target user group from users of the video website includes:
acquiring user portrait information of each user in the video website, wherein the user portrait information comprises scores of the users in the video website in each designated dimension;
and adding the users with the scores larger than a preset threshold value in the preset dimension into the target user group according to the user portrait information of each user in the video website.
In a possible implementation manner, the determining, according to behavior information of each user in the target user group on a target video, a feature of the target video includes:
acquiring behavior information of each user in the video website to the target video from the automatic reference notification pingback data;
screening out the behavior information of each user in the target user group to the target video from the behavior information of each user in the video website to the target video;
and determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video.
In a second aspect, an embodiment of the present application further provides an apparatus for determining a video feature, including:
the screening module is used for screening a target user group from users of the video website, wherein the target user group comprises users with the correlation degree reaching a preset threshold value, and the preset dimension is a category to which the target video belongs;
and the determining module is used for determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video.
In one possible implementation, the apparatus further includes: setting a module;
the setting module is configured to set the preset dimension and the preset value, where the user whose association degree with the preset dimension reaches the preset threshold is the user whose score in the preset dimension reaches the preset threshold.
In a possible implementation manner, the screening module is specifically configured to obtain user portrait information of each user in the video website, where the user portrait information includes scores of the users in the video website in each specified dimension; and adding the users with the scores larger than a preset threshold value in the preset dimension into the target user group according to the user portrait information of each user in the video website.
In a possible implementation manner, the determining module is specifically configured to obtain behavior information of each user in the video website on the target video from an automatic citation notification pingback data; screening out the behavior information of each user in the target user group to the target video from the behavior information of each user in the video website to the target video; and determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video.
In a third aspect, an embodiment of the present application provides a video server, where the video server includes: a processor and a machine-readable storage medium storing machine-executable instructions executable by the processor, the processor being caused by the machine-executable instructions to: the method of determining video characteristics as described in the first aspect is implemented.
In a fourth aspect, the present application further provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the method for determining video features in the first aspect.
In a fifth aspect, embodiments of the present application further provide a computer program product containing instructions, which when run on a computer, cause the computer to perform the method for determining video characteristics described in the first aspect.
Compared with the prior art that the characteristics of the target video are determined according to the behavior information of the users in the whole video website, the method and the device for determining the video characteristics screen out the users meeting the preset conditions in the preset dimension as the target user group, and the preset dimension is associated with the target video, so that the characteristics of the target video can be determined according to the behavior information of the part of users associated with the target video, and therefore the behavior data of the users irrelevant to the target video can be eliminated, and the determined characteristics of the target video are more accurate.
Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a flowchart of a method for determining video characteristics according to an embodiment of the present disclosure;
fig. 2 is an exemplary schematic diagram of a method for determining video characteristics according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an apparatus for determining video characteristics according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a video server according to an embodiment of the present application.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
In order to improve the accuracy of the determined video features, an embodiment of the present application provides a method for determining video features, where the method is performed by a video server, and as shown in fig. 1, the method includes:
s101, screening out a target user group from users of the video website.
The target user group includes users whose degree of association with a preset dimension reaches a preset threshold, where the preset dimension is a category to which the target video belongs, for example, the preset dimension may be a channel to which the target video belongs, or a country, a year, and the like to which the target video belongs.
Before screening, a preset dimension and a preset threshold value can be set, and the preset condition is that the degree of association between the user and the preset dimension reaches the preset threshold value. The degree of association between the user and the preset dimension may be represented by a score of the user in the preset dimension, that is, the user whose degree of association with the preset dimension reaches the preset threshold is a user whose score in the preset dimension is greater than the preset threshold. Specifically, the preset dimension may be set according to the video feature to be determined, for example, if the video feature to be determined is a popularity of a certain documentary, the preset dimension is a documentary.
The screening method comprises the following steps: the method comprises the steps of obtaining user portrait information of each user in a video website, and adding the users with scores larger than a preset threshold value in a preset dimension into a target user group according to the user portrait information of each user in the video website.
The user portrait information includes scores of users in video websites in various designated dimensions, for example, taking a video channel as an example, if the scores of a certain user in several channels of sports, entertainment, movies and documentaries are all 0.98, and the score in the channels of information and mother and infant is 0.1, it indicates that the user is a user who likes to watch sports, entertainment and movies, but does not like to watch information and related information of mother and infant.
For example, if the video feature to be determined is the popularity of a certain documentary, users with scores greater than a preset threshold (e.g. 0.7) on the channel of the documentary may be screened out, and this part of users may be grouped into a target user group.
For another example, if the target video is a sports video, in order to determine the click rate of the sports video, users whose scores on the sports channel are greater than a preset threshold (e.g., 0.7) may be screened out, and this part of users may be grouped into the target user group.
S102, determining the characteristics of the target video according to the behavior information of each user in the target user group to the target video.
The behavior information of the user on the video may include clicking, browsing, playing, and the like.
Various behaviors of the user in the video website can be sent to the video server in a pingback mode, and behavior information of the user on the video is recorded in pingback data.
In the embodiment of the application, the behavior information of all users in the video website to the target video can be obtained from the pingback data, then the behavior information of each user in the target user group to the target video is screened out from the behavior information of each user in the video website to the target video, and the characteristics of the target video are determined according to the behavior information of each user in the target user group to the target video.
For example, if the target video is a sports video, the click rate of the target video may be determined according to the click rate and the display times of each user in the target user group, where the target video corresponds to the sports video, and the display times of each user in the target user group where information of the target video is displayed in the video network website. The click rate of the target video can reflect the popularity of the target video, and the characteristics of the target video can be reflected.
Compared with the prior art that the characteristics of the target video are determined according to the behavior information of the users in the whole video website, the method for determining the video characteristics screens out the users with the association degree with the preset dimensionality reaching the preset threshold value as the target user group, because the preset dimensionality is the category to which the target video belongs, the association degree between the users with the association degree with the preset dimensionality reaching the preset threshold value and the target video is higher, and the characteristics of the target video are determined according to the behavior information of the part of the users with the higher association degree with the target video, so that the behavior data of the users irrelevant to the target video can be eliminated, and the determined interest association degree between the target video and the users is higher.
The following describes a method for determining video features according to an embodiment of the present application with reference to a specific example, as shown in fig. 2, assuming that the quality of a certain movie (e.g., movie a) is measured by information such as a click rate of the movie, user portrait information of each user in a video website may be obtained, where the user portrait information includes scores of the users in each dimension, and the dimensions of the movie, the sports, the tv series, and the documentary are exemplarily shown in fig. 2.
After the user portrait information is obtained, users with scores greater than 0.7 in the dimension of the movie can be screened out according to the user portrait information, the users form a target user group, and then behavior information of each user in the target user group to the movie A is screened out from user behavior data of the whole video website. In this scenario, the acquired behavior information includes the click rate, browsing amount, and staying time of the user in the target user group on movie a.
The click rate is the total number of times that all users in the target user group click on movie a within a preset time period.
The browsing volume is the total number of times that all users in the target user group browse the information of movie a within a preset time period.
The stay time refers to the average stay time of each user in the target user group after clicking on movie a in the playing interface of movie a.
Further, the click rate of movie a can be calculated according to the click rate, and the quality of movie a can be determined according to the click rate, the browsing amount and the average duration of movie a.
Optionally, after behavior information of the user in the target user group on the movie a is acquired, the feature of the movie a may be calculated through feature calculation methods such as dirty data cleaning, feature filtering, feature combination, feature calculation, and normalization processing.
Therefore, by adopting the method, in order to measure the quality of the movie, users with higher scores in movie dimensions can be screened out, the behavior information of the part of users to the movie A is adopted to determine the characteristics of the movie A, and the part of users like watching the movie at ordinary times, so that the behavior information of the part of users to the movie A is more representative.
Corresponding to the foregoing method embodiment, an embodiment of the present application further provides an apparatus for determining video features, as shown in fig. 3, the apparatus includes: a screening module 301 and a determination module 302.
The screening module 301 is configured to screen a target user group from users of a video website, where the target user group includes users whose association degree with a preset dimension reaches a preset threshold, and the preset dimension is a category to which a target video belongs;
the determining module 302 is configured to determine characteristics of the target video according to behavior information of each user in the target user group on the target video.
Optionally, the apparatus further comprises: a setting module 303;
the setting module 303 is configured to set a preset dimension and a preset threshold, where a user whose association degree with the preset dimension reaches the preset threshold is a user whose score in the preset dimension reaches the preset threshold.
Optionally, the screening module 301 is specifically configured to obtain user portrait information of each user in the video website, where the user portrait information includes scores of the users in the video website in each specified dimension; and adding the users with the scores larger than a preset threshold value in a preset dimension into a target user group according to the user portrait information of each user in the video website.
Optionally, the determining module 302 is specifically configured to obtain behavior information of each user in the video website on the target video from the automatic citation notification pingback data; screening out the behavior information of each user in the target user group to the target video from the behavior information of each user to the target video in the video website; and determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video.
The embodiment of the present invention further provides a video server, as shown in fig. 4, which includes a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 complete mutual communication through the communication bus 404,
a memory 403 for storing a computer program;
the processor 401, when executing the program stored in the memory 403, implements the steps performed by the video server in the above method embodiments.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the video server and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
In yet another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the above-mentioned video feature determination methods.
In a further embodiment of the present invention, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the method of determining a video characteristic of any of the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (6)
1. A method for determining video characteristics, comprising:
screening a target user group from users of a video website, wherein the target user group comprises users of which the association degree with a preset dimension reaches a preset threshold value, and the preset dimension is a category to which a target video belongs;
determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video;
the method for screening out the target user group from the users of the video website comprises the following steps:
acquiring user portrait information of each user in the video website, wherein the user portrait information comprises scores of the users in the video website in each designated dimension;
adding users with scores larger than the preset threshold value in the preset dimension into the target user group according to the user portrait information of each user in the video website;
before the screening out the target user group from the users of the video website, the method further comprises:
and setting the preset dimension and the preset threshold, wherein the user with the correlation degree with the preset dimension reaching the preset threshold is the user with the score of the preset dimension reaching the preset threshold.
2. The method according to claim 1, wherein the determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video comprises:
acquiring behavior information of each user in the video website to the target video from the automatic reference notification pingback data;
screening out the behavior information of each user in the target user group to the target video from the behavior information of each user in the video website to the target video;
and determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video.
3. An apparatus for determining a video feature, comprising:
the screening module is used for screening a target user group from users of the video website, wherein the target user group comprises users of which the association degree with a preset dimension reaches a preset threshold value, and the preset dimension is a category to which a target video belongs;
the determining module is used for determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video;
the screening module is specifically used for acquiring user portrait information of each user in the video website, wherein the user portrait information comprises scores of the users in the video website in each specified dimension; adding users with scores larger than the preset threshold value in the preset dimension into the target user group according to the user portrait information of each user in the video website;
the setting module is used for setting the preset dimensionality and the preset threshold, and the user with the correlation degree with the preset dimensionality reaching the preset threshold is the user with the score of the preset dimensionality reaching the preset threshold.
4. The apparatus of claim 3,
the determining module is specifically configured to acquire behavior information of each user in the video website on the target video from the automatic citation notification pingback data; screening out the behavior information of each user in the target user group to the target video from the behavior information of each user in the video website to the target video; and determining the characteristics of the target video according to the behavior information of each user in the target user group on the target video.
5. The video server is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing the communication between the processor and the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of claim 1 or 2 when executing a program stored in the memory.
6. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of claim 1 or 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811183929.7A CN109168044B (en) | 2018-10-11 | 2018-10-11 | Method and device for determining video characteristics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811183929.7A CN109168044B (en) | 2018-10-11 | 2018-10-11 | Method and device for determining video characteristics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109168044A CN109168044A (en) | 2019-01-08 |
| CN109168044B true CN109168044B (en) | 2021-07-23 |
Family
ID=64877830
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811183929.7A Active CN109168044B (en) | 2018-10-11 | 2018-10-11 | Method and device for determining video characteristics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109168044B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111222011B (en) * | 2020-01-06 | 2023-11-14 | 腾讯科技（深圳）有限公司 | Video vector determining method and device |
| CN113822684B (en) * | 2021-09-28 | 2023-06-06 | 北京奇艺世纪科技有限公司 | Black-birth user identification model training method and device, electronic equipment and storage medium |
| CN114926771A (en) * | 2022-06-01 | 2022-08-19 | 北京字节跳动网络技术有限公司 | Video identification method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103501434A (en) * | 2013-09-17 | 2014-01-08 | 北京奇艺世纪科技有限公司 | Method and device for analyzing quality of video |
| CN104021162A (en) * | 2014-05-28 | 2014-09-03 | 小米科技有限责任公司 | Method and device for grading multimedia resource |
| CN104506894A (en) * | 2014-12-22 | 2015-04-08 | 合一网络技术(北京)有限公司 | Method and device for evaluating multi-media resources |
| CN105701226A (en) * | 2016-01-18 | 2016-06-22 | 合网络技术（北京）有限公司 | Multimedia resource assessment method and device |
| CN106339409A (en) * | 2016-08-10 | 2017-01-18 | 乐视控股（北京）有限公司 | Method and device for acquiring corpus information of user |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060218187A1 (en) * | 2005-03-25 | 2006-09-28 | Microsoft Corporation | Methods, systems, and computer-readable media for generating an ordered list of one or more media items |
| US10089592B2 (en) * | 2010-12-29 | 2018-10-02 | Comcast Cable Communications, Llc | Measuring video asset viewing |
| SG11201405084SA (en) * | 2012-02-21 | 2014-10-30 | Ooyala Inc | Automatically recommending content |
| CN106599022B (en) * | 2016-11-01 | 2019-12-10 | 中山大学 | User portrait forming method based on user access data |
| CN108154379B (en) * | 2016-12-06 | 2021-03-26 | 腾讯科技（深圳）有限公司 | Media information publishing method and device |
| CN107368519A (en) * | 2017-06-05 | 2017-11-21 | 桂林电子科技大学 | A kind of cooperative processing method and system for agreeing with user interest change |
| CN107341206B (en) * | 2017-06-23 | 2019-11-29 | 南京甄视智能科技有限公司 | The method for constructing accurately user's portrait system based on multiple data sources |
| CN107885889A (en) * | 2017-12-13 | 2018-04-06 | 聚好看科技股份有限公司 | Feedback method, methods of exhibiting and the device of search result |
-
2018
- 2018-10-11 CN CN201811183929.7A patent/CN109168044B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103501434A (en) * | 2013-09-17 | 2014-01-08 | 北京奇艺世纪科技有限公司 | Method and device for analyzing quality of video |
| CN104021162A (en) * | 2014-05-28 | 2014-09-03 | 小米科技有限责任公司 | Method and device for grading multimedia resource |
| CN104506894A (en) * | 2014-12-22 | 2015-04-08 | 合一网络技术(北京)有限公司 | Method and device for evaluating multi-media resources |
| CN105701226A (en) * | 2016-01-18 | 2016-06-22 | 合网络技术（北京）有限公司 | Multimedia resource assessment method and device |
| CN106339409A (en) * | 2016-08-10 | 2017-01-18 | 乐视控股（北京）有限公司 | Method and device for acquiring corpus information of user |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109168044A (en) | 2019-01-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109145210B (en) | Information recommendation method and device and storage medium | |
| CN109168044B (en) | Method and device for determining video characteristics | |
| US9098551B1 (en) | Method and system for ranking content by click count and other web popularity signals | |
| CN109753601B (en) | Method and device for determining click rate of recommended information and electronic equipment | |
| CN111683292B (en) | Video playing method and device | |
| CN110290400B (en) | Suspicious brushing amount video identification method, real playing amount estimation method and device | |
| CN109165691B (en) | Training method and device for model for identifying cheating users and electronic equipment | |
| CN109451333B (en) | Bullet screen display method, device, terminal and system | |
| CN107529093B (en) | Method and system for detecting playing amount of video file | |
| CN110941738A (en) | Recommendation method and device, electronic equipment and computer-readable storage medium | |
| CN111225246A (en) | Video recommendation method and device and electronic equipment | |
| CN111753239B (en) | Resource distribution method and device, electronic equipment and storage medium | |
| CN111107416A (en) | Bullet screen shielding method and device and electronic equipment | |
| CN111683143B (en) | Message pushing method and device, electronic equipment and computer readable storage medium | |
| CN111327609B (en) | Data auditing method and device | |
| CN112785333A (en) | Advertisement putting method, electronic device, storage medium and device | |
| CN111597448A (en) | Information heat determining method and device and electronic equipment | |
| CN112995765B (en) | Network resource display method and device | |
| CN110442801B (en) | Method and device for determining concerned users of target events | |
| CN111597380B (en) | Recommended video determining method and device, electronic equipment and storage medium | |
| CN110996142B (en) | Video recall method and device, electronic equipment and storage medium | |
| CN111079061B (en) | Data processing method and electronic equipment | |
| CN111080349B (en) | Method, device, server and medium for identifying multiple devices of same user | |
| CN111178942B (en) | Advertisement shielding method, device, equipment and storage medium | |
| US20140236927A1 (en) | Internet presence scoring |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231205 Address after: 200335 room 205, floor 2, building 9, No. 365, Linhong Road, Changning District, Shanghai Patentee after: Shanghai iqiyi New Media Technology Co.,Ltd. Address before: 10 / F and 11 / F, Hongcheng development building, 2 Haidian North 1st Street, Haidian District, Beijing 100080 Patentee before: BEIJING QIYI CENTURY SCIENCE & TECHNOLOGY Co.,Ltd. |