Context boosting collaborative recommendations

摘要

This paper describes the research underpinning a networked application for the delivery of personalised streams of music over the Internet. The initial system used automated collaborative filtering (ACF), a ‘content-less’ approach to recommend new music to users. We show how we have improved on this basic technique by leveraging a light content-based technique that attempts to capture the user's current listening ‘context’. This involves a two-stage retrieval process where ACF recommendations are ranked according to the user's current interests. Finally, we demonstrate an on-line evaluation strategy that pits the ACF strategy against the context-boosted strategy in a real-time competition.