Video recommendation method based on timing sequence data mining

A technology of video recommendation and time series data, applied in the field of video technology and video recommendation, can solve the problem of reducing the accuracy of recommendation, and achieve the effect of improving accuracy and efficiency

Active Publication Date: 2015-09-23
INST OF INFORMATION ENG CHINESE ACAD OF SCI
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

If the user's interest migration is not taken into account when making personalized video recommendations to the user, it means that the system may only recommend the user his previous favorite videos, but it does not match his recent interests, which reduces the accuracy of the recommendation

Method used

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  • Video recommendation method based on timing sequence data mining
  • Video recommendation method based on timing sequence data mining
  • Video recommendation method based on timing sequence data mining

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Embodiment Construction

[0020] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described below through specific embodiments and accompanying drawings.

[0021] The present invention uses third-party data (such as using Douban scores) to discover user interest migration, determines the time window size of training data, thereby shielding the impact of user interest migration, and then utilizes a trust model based on Random Walker to perform personalized video recommendation. figure 1 Is the general flowchart of the method of the present invention.

[0022] 1. Discovery of user interest migration based on third-party data

[0023] As mentioned above, the present invention uses the time window method to overcome the impact of user interest migration on recommendation results. A fixed-size time window cannot accurately reflect user interest migration. Therefore, in order to obtain a suitable time ...

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Abstract

The invention relates to a video recommendation method based on timing sequence data mining. The method comprises the steps that 1) video interest gradient change of a user is analyzed via third party data and a user interest gradient curve is obtained, and the singular points of the user interest gradient curve act as time points of user interest migration; 2) the latest interest migration time points of the user are confirmed, and user-item scores after the latest interest migration time points of the user are acquired so that a user-item score matrix meeting the current interest of the user within a selected time window is established; and 3) user customized video recommendation is performed by using a random walk model on the basis of the user-item score matrix. An interest migration problem in the customized video recommendation is considered, and customized video recommendation is performed though integration of a time window method and on the basis of the Random Walker trust degree model so that video recommendation accuracy and efficiency are enhanced.

Description

technical field [0001] The invention belongs to the field of video technology and video recommendation technology, and in particular relates to a video recommendation method based on time series data mining. Background technique [0002] With the development of communication technology, the available bandwidth of the network has increased rapidly. Watching videos has become a common thing for PC users and even mobile terminal users. This has also brought about the rapid development of the Internet video industry, and the video recommendation system is an Internet video important part of the service. The original video recommendation system makes recommendations based on the similarity between videos, and two users watching the same movie will give the same recommendation results. However, with the successful application of personalized recommendation methods in search engines and shopping websites in recent years, the field of video recommendation has also paid more and mor...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N21/258H04N21/466
CPCH04N21/25891H04N21/4667H04N21/4668
Inventor 杨凡牛温佳胡玥毛志张博敖吉谭建龙郭莉
Owner INST OF INFORMATION ENG CHINESE ACAD OF SCI
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