Data fusion based video program recommendation method

Abstract

A data fusion based video program recommendation method comprises the following steps: 1) collection of behavioral data of users: collecting the behavioral data of the users in browsing video websites and storing the behavioral data in a database; 2) data fusion: converting the behavioral data of a user in browsing a video program into the interest value of the user in the video program; and 3) program recommendation: according to the interest value of the user in a few programs, recommending the programs in which the user has high interest value to other users having the same favorites, wherein the behavioral data in browsing comprise behavioral data in playing. The duration ratio r is obtained by dividing the duration of the programs played by the users by the total duration of the programs. The higher the duration ratio r is, the higher the interest value is. As long as the users play the programs and other behavioral data of the users related to the interest value are fused, the program lists to be recommended can be generated more accurately and the degree of satisfaction of the users with the recommended programs is greatly improved.

Description

technical field [0001] The invention relates to a method for recommending video programs. Background technique [0002] With the development of the video network, watching video programs through the network has become one of the purposes of surfing the Internet for many network users. How to attract users to the greatest extent has become an important issue for video sites to consider. An effective method is to recommend video programs to users according to their viewing preferences. [0003] After analysis, the user usually has preferences for watching certain types of programs. For example, some users like to watch sports programs, some users like to watch entertainment programs, and some users only like to watch kung fu programs and so on. [0004] At the Tenth International Internet Conference (Proceedings of the 10 th international conference on World Wide Web), Badru and George proposed an item-based collaborative filtering recommendation system (Item-based Collab...

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Problems solved by technology

2. Even if a small number of users rate video programs, the proportion of these rated programs in all video programs is very small, usually less than 1%

The above-mentioned difficulties will lead to the "cold start problem" in the above-mentioned collaborative filtering recommendation system, (see "Category-Based Recommendation - A Method to Solve the Cold Start Problem in Collaborative Recommendation" published in "Computer Research and Development" ), this kind of cold start problem will cause the recommendation system to fail to work properly and produce wrong recommendation results

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