A joint framework for collaborative and content filtering

Justin Basilico, Thomas Hofmann

Abstract: This paper proposes a novel, unified, and systematic approach to combine collaborative and content-based filtering for ranking and user preference prediction. The framework incorporates all available information by coupling together multiple learning problems and using a suitable kernel or similarity function between user-item pairs. We propose and evaluate an on-line algorithm (JRank)that generalizes perceptron learning using this framework and shows significant improvement over other approaches.