Evaluation Metrics for Recommender Systems

In this article, the existed evaluation metrics for recommender systems are reviewed and the new progresses in this field are summarized from four aspects: accuracy, diversity, novelty and coverage. The merits, weaknesses and applicable conditions of different evaluation metrics are analized. The focus is concentrated on the importance of rank and some representative rank-sensitive metrics. The user-centric recommender systems are discussed and some important open problems are outlined as future possible directions.