Collaborative filtering algorithm combining movie popularity and viewing time
Qian Zejun，Liu Runran
(Alibaba Business School, Hangzhou Normal University, Hangzhou 311121, China)
Abstract： As the core of the collaborative filtering recommendation algorithm, similarity evaluation is still difficult to fully utilize evaluation data in all dimensions, despite researchers constantly improving it. In this paper, aiming at this challenge, the mutual influence between users and movies is taken as the starting point, the possible self consistent logic between the two is explored, and a formula called Movie Popularity Weight (MPW) calculation formula is proposed to calculate the weight of movies. Then, taking the viewing time of users as the research object, the relationship between the change of viewing preference and the viewing time sequence of users is explored, and combined with the theory of Kendall correlation coefficient, a formula called Consistency in Viewing Sequence (CVS) calculation formula is proposed. Finally, the traditional similarity algorithm is improved by using the above research content, and the improved similarity algorithm is validated by using two datasets, one is the Netflix Prize dataset, while the other one is built based on publicly available data from Douban.com called Douban Movie K5 dataset. The experimental result shows that the improved similarity algorithm has higher recommendation accuracy.
Key words : recommendation algorithm; collaborative filtering; similarity algorithm; movie popularity; viewing time
