Collaborative Filtering-Based Recommendation System Using Time Decay Model

Online collaborative movie recommendation systems attempt to help customers accessing their favourable movies by gathering exactly comparable neighbors between the movies from their chronological identical ratings. Collaborative filtering-based movie recommendation systems require viewer-specific data, and the need for collecting viewer-specific data diminishes the effectiveness of the recommendation. To solve this problem, the authors employ an effective multi-armed bandit called upper confidence bound, which is applied to automatically recommend the movies for the users. In addition, the concept of time decay is provided in a mathematical definition that redefines the dynamic item-to-item similarity. Then, two patterns of time decay are analyzed, namely concave and convex functions, for simulation. The experiment test the MovieLens 100K dataset. The proposed method attains a maximum F-measure of 98.45 whereas the existing method reaches a minimum F-measure of only 95.60. The presented model adaptively responds to new users, can provide a better service, and generate more user engagement.