In this work we present topic diversification, a novel method designed to balance and diversify personalized recommenda- tion 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 rec- ommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm. Our work builds upon prior research on recommender sys- tems, looking at properties of recommendation lists as en- tities 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 diver- sity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evalu- ate our method using book recommendation data, including online analysis on 361, 349 ratings and an online study in- volving more than 2, 100 subjects.