Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation

These preferences could be largely reflected by the ratings on the online items with explicit scores. Both of positive and negative ratings are helpful for recommender systems to provide relevant items to a target user.

Based on the empirical analysis of three real-world movie-rating data sets, we observe that users' rating criterions change over time, and past positive and negative ratings have different influences on users' future preferences. Given this, we propose a recommendation model on a session-based temporal graph, considering the difference of long- and short-term preferences, and the different temporal effect of positive and negative ratings.