Video recommending method based on time factor

Abstract

The invention provides a video recommending method based on a time factor. The method comprises the steps that a corresponding user-video scoring matrix is constructed by collecting operation scoring information on videos by users and recording every operation time, and the operation behaviors of the users are analyzed through three different time angles such as the time span, the time sequence, the time period and the like, so that an interest model of the users is built, wherein according to the time span, the larger the time span of the last video watching time is, the smaller the interest in the videos of the users is; according to the time sequence, the sequence in which the users watch the videos refers to the internal relation between the videos; according to the time period, when the users watch the videos more than once, the influence is caused every time the videos are watched; finally, a system recommends the videos in which the users interest according to the latest interests of the users. According to the video recommending method, various points of view of time are comprehensively considered, and the constructed interest model can better adapt to the user interest shifting.

Description

technical field [0001] The invention relates to the field of recommendation methods, in particular to a video recommendation method based on a time factor. Background technique [0002] With the development of video technology, a large number of videos such as animations, movies, TV dramas, and variety shows are produced every day. At the same time, with the development of Internet technology, people are provided with a more convenient way to watch a huge number of videos. YouTube, Youku and other video sites have an equivalent amount of video clips uploaded every moment, and the problem of video information overload is becoming more and more obvious, making it impossible for people to quickly obtain videos that meet their preferences. In order to deal with this problem, the video recommendation method came into being, and has become a very potential method to solve the problem of information overload in the music field. [0003] The video recommendation method is essential...

AI Technical Summary

Problems solved by technology

This is mainly manifested in the following points: 1. The system data is relatively sparse, and it is impossible to accurately find users with the same interests as users, so as to perform collaborative recommendations; 2. The interests of users are constantly changing, and the recommended videos can match the overall interests of users. However, it cannot adapt to the user's current interests; 3. Videos are different from other items. For the same video, users have multiple viewing behaviors such as reviewing. Therefore, while recommending new videos, old videos are also updated according to the user's viewing records available for recommendation

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