document: Improving recommendation lists through topic diversification In this work we present topic diversification , a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user 's complete spectrum of interests . Though being detrimental to average accuracy , we show that our method improves user satisfaction with recommendation lists , in particular for lists generated using the common item-based collaborative filtering algorithm . Our work builds upon prior research on recommender systems , looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations . We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity . We evaluate our method using book recommendation data , including offline analysis on 361 , ! , 349 ratings and an online study involving more than 2 , ! , 100 subjects .
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