Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System

Most recommender systems usually combine several recommendation methods to enhance the accuracy. Collaborative filtering (CF) is a best-known personalized technique. While temporal association rule-based algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting performance. Aiming at above problems, time-aware CF hybrid system, TP-HR, proposed. proposed considers evolving features of feedback. And similar neighbors selecting measure item rating prediction function keep track dynamics preferences. time context behaviors when mining effective rules. Experimental results on real datasets show feasibility performance improvement system compared other baseline approaches.