%0 
%0 Conference Proceedings
%A Ziegler, Cai-Nicolas; McNee, Sean; Konstan, Joseph & Lausen, Georg
%D 2005
%T Improving Recommendation Lists Through Topic Diversification
%E 
%B Proceedings of the 14th International World Wide Web Conference
%C Chiba, Japan
%I ACM Press
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 May
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 null
%2 
%3 inproceedings
%4 
%# 
%$ 
%F ziegler2005improving
%K diploma_thesis, eventually_useful
%X 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.
%Z 
%U 
%+
%^