Recommendations without user preferences: a natural language processing approach

We examine the problems with automated recommendation systems when information about user preferences is limited. We equate the problem to one of content similarity measurement and apply techniques from Natural Language Processing to the domain of movie recommendation. We describe two algorithms, a naïve word-space approach and a more sophisticated approach using topic signatures, and evaluate their performance compared to baseline, gold standard, and commercial approaches. KEYWORDS Recommender systems, Natural Language Processing, content-based, similarity metrics

• Type: Conference
• Year: 2003
• Where: IUI
• Authors: Michael Fleischman, Eduard H. Hovy
• Keywords: Content Similarity Measurement, IUI 2003, Naïve Word-space Approach, Natural Language Processing, Software Engineering