Collaborative filtering recommendation method introducing video popularity and user interest change

Abstract

The invention discloses a collaborative filtering recommendation method introducing video popularity and user interest change. The method comprises the following steps of: acquiring and processing user behavior data, and thus obtaining a user-video binary incidence matrix; acquiring a video popularity weight and a user interest weight on the basis of the matrix, and introducing the video popularity weight and the user interest weight into a user similarity calculation process; searching the first K neighbors maximally similar to a target user, and predicting an interest value of the target user in a video which does not generate an effective behavior according to the magnitude of the similarity between the target user and a neighbor user; and selecting N videos with the maximum interest value to form a recommendation list, and providing a personalized recommendation for the user. In full consideration of the characteristics of difference in the popularity of the videos in the system and the time-dependent change of user interest, the method is in accordance with an objective fact, so that the user similarity can be accurately calculated, the quality of a collaborative filtering recommendation is improved, and a personalized video recommendation in accordance with the user interest is provided for a video user.

Description

technical field [0001] The invention belongs to the technical field of rich media personalized recommendation, and more specifically relates to a collaborative filtering recommendation method that introduces changes in video popularity and user interest. Background technique [0002] With the development of the Internet and its applications, the Internet has ushered in the era of "information explosion". The search technology represented by Google and Baidu can no longer meet the needs of users. First, users may not find the information they want through search engines. , Second, users may not be able to actively express their needs into appropriate words to make the search engine work. As a result, the recommendation system came into being, which can collect the user's historical behavior and feedback information, find resources that meet the user's interests based on this information, and then make personalized recommendations for the user. [0003] Recommendation technol...

AI Technical Summary

Problems solved by technology

The second is to ignore the change of user interest over time

Because the behavior of different users watching video programs in a small time range can better reflect the similarity of users than the behavior of a large time span, so the traditional similarity calculation method does not consider the change of user interest, which is not in line with objective facts.

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