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Network video ordering method based on focusing time of users

A technology that focuses on time and network video, applied in the field of computer network search, can solve disputed problems, etc.

Inactive Publication Date: 2009-03-11
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although it has been mentioned more and more in recent research, it is still debated whether it can really reflect user intent

Method used

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  • Network video ordering method based on focusing time of users
  • Network video ordering method based on focusing time of users
  • Network video ordering method based on focusing time of users

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0081] The flow structure of the network video sorting method based on the user's attention time of the present invention is as follows: figure 1 shown. The personalized sorting system includes two parts, the client and the server, the client 20, a custom browser to obtain the user's attention time, the server includes 30, a sample collection module, 40, attention time correction, 50, user database and 60 , video database, 70, query interface, 80, traditional engine module, 90, video preprocessing module, 100, video comparison module, 110, attention time prediction module, 120, sorting module.

[0082] Customize the browser 20 to track and analyze the user's mouse movement to obtain the user's attention time on each video summary; monitor the video playback controls on the webpage to obtain the user's playing time of each video segment.

[0083] The sample collection module 30 stores the sample data sent by the client into the database corresponding to the user, if a certain ...

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Abstract

The invention discloses a network video ordering method based on user concerned time, which utilizes the user concerned time to carry out an individualized improvement against the searching results obtained by a network video searching engine so as to render the search ordering results of network videos to better meet needs of the user. The network video ordering method based on the user concerned time comprises the following steps: 1) the concerned time of the user for every network video in the network video searching results is obtained by the use of a user-defined web page browser; 2) every user concerned time obtained is corrected; 3) the user concerned time of every key frame in unknown network videos is predicted based on image similarity; 4) the user concerned time of unknown network videos is calculated through the user concerned time of every key frame of the video; and 5) the network videos obtained by the network video searching engine are respectively reordered by the use of the user concerned time obtained or predicted. The network video ordering method based on the user concerned time effectively combines the favorite of the user with the network video searching process, thus rendering the final network video ordering results to be closer to the ideal ordering expected by the user.

Description

technical field [0001] The invention relates to the field of computer network search, in particular to a network video sorting method based on user attention time. Background technique [0002] Existing personalization engines rely on user feedback, which can be divided into explicit feedback and implicit feedback. We can get the user's preference characteristics from both kinds of feedback. However, users are generally unwilling to provide explicit feedback, so more and more research is now turning to implicit feedback. Studies have shown that implicit feedback can well reflect the user's search intention, and user preferences obtained from a large number of implicit feedback are often more reliable than explicit feedback. [0003] Query history: In modern research, the most frequently used implicit feedback is the user's query history. Google's personalized search (http: / / www.google.com / psearch) is based on the user's query history. In general, algorithms based on quer...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 徐颂华江浩刘智满潘云鹤
Owner ZHEJIANG UNIV
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