A joint framework for collaborative and content filtering

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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Information Filtering

• Type: Conference
• Year: 2004
• Where: SIGIR
• Authors: Justin Basilico, Thomas Hofmann
• Keywords: Multiple Learning Problems, SIGIR 2004, Systematic Approach, User Preference Prediction