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You're in PublicationsCoverage, Redundancy and Size-awareness in Genre Diversity for Recommender Systems

 

Coverage, Redundancy and Size-awareness in Genre Diversity for Recommender Systems

There is increasing awareness in the Recommender Systems
field that diversity is a key property that enhances the usefulness of recommendations. Genre information can serve as a means to measure and enhance the diversity of recommendations and is readily available in domains such as movies, music or books. In this work we propose a new Binomial framework for defining genre diversity in recommender systems that takes into account three key properties: genre
coverage genre redundancy and recommendation list
size-awareness.
 
We show that methods previously proposed for measuring
and enhancing recommendation diversity –including those
adapted from search result diversification– fail to address
adequately these three properties. We also propose an ef-
ficient greedy optimization technique to optimize Binomial
diversity. Experiments with the Netflix dataset show the
properties of our framework and comparison with state of
the art methods.