Collaborative filtering algorithm based on user interest change
Na Song, Qin Lu · 2019
The collaborative filtering algorithm can provide personalized service recommendations based on the user's personal interests. In view of the shortcomings of traditional similarity measurement methods for user interest changes, this paper proposes a collaborative filtering algorithm based on user interest changes. When calculating the user similarity, the time penalty function is added to the traditional Pearson correlation coefficient, and then the score prediction is used to obtain the target user's rating data to make recommendations for the user. The improved algorithm can adapt to the change of user interest under the change of time. The experimental results on the MovieLens dataset show that compared with the traditional algorithm, the proposed algorithm can make recommendations for users more accurately.