Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model

Abstract. We use a hierarchical Bayesian approach to model user preferences in different contexts or settings. Unlike many previous recommenders, our approach is content-based. We assume that for each context, a user has a different set of preference weights which are linked by a common, “generic context” set of weights. The approach uses Expectation Maximization (EM) to estimate both the generic context weights and the context specific weights. This improves upon many current recommender systems that do not incorporate context into the recommendations they provide. In this paper, we show that by considering contextual information, we can improve our recommendations, demonstrating that it is useful to consider context in giving ratings. Because the approach does not rely on connecting users via collaborative filtering, users are able to interpret contexts in different ways and invent their own contexts. Key words: recommender system, Expectation Maximization, hierarchical Baye...

• Type: Conference
• Year: 2009
• Where: AI
• Authors: Daniel Pomerantz, Gregory Dudek
• Keywords: AI 2009, Artificial Intelligence, Expectation Maximization, Generic Context, Generic Context Weights