On User Recommendations Based On Multiple Cues


In this paper we present an overview of a recommender system that attempts to predict user preferences based on several sources including prior choices and selected user-defined features. By using a combination of collaborative filtering and semantic features, we hope to provide performance superior to either alone. Further, our set of semantic features is acquired and updated using a learning-based procedure that avoids the need for manual knowledge-engineering. Our system is implemented in a web-based application server environment and can be used with abitrary domains, although the test data reported here is restricted to recommendations of movies.