  Recommender systems use data on past user preferences to predict possible
future likes and interests. A key challenge is that while the most useful
individual recommendations are to be found among diverse niche objects, the
most reliably accurate results are obtained by methods that recommend objects
based on user or object similarity. In this paper we introduce a new algorithm
specifically to address the challenge of diversity and show how it can be used
to resolve this apparent dilemma when combined in an elegant hybrid with an
accuracy-focused algorithm. By tuning the hybrid appropriately we are able to
obtain, without relying on any semantic or context-specific information,
simultaneous gains in both accuracy and diversity of recommendations.
