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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.