Improving recommendation lists through topic diversification Export

@inproceedings{citeulike:763598, abstract = {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.}, address = {New York, NY, USA}, author = {Ziegler, Cai-Nicolas and Mcnee, Sean M. and Konstan, Joseph A. and Lausen, Georg}, booktitle = {WWW '05: Proceedings of the 14th international conference on World Wide Web}, citeulike-article-id = {763598}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1060754}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1060745.1060754}, doi = {10.1145/1060745.1060754}, isbn = {1595930469}, keywords = {diversification, recommendation}, pages = {22--32}, posted-at = {2009-02-18 08:53:33}, priority = {2}, publisher = {ACM Press}, title = {Improving recommendation lists through topic diversification}, url = {http://dx.doi.org/10.1145/1060745.1060754}, year = {2005} }

TY - CONF ID - citeulike:763598 L3 - citeulike-article-id:763598 N2 - 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. SN - 1595930469 AD - New York, NY, USA EP - 32 TI - Improving recommendation lists through topic diversification PB - ACM Press T2 - WWW '05: Proceedings of the 14th international conference on World Wide Web SP - 22 KW - diversification KW - recommendation AU - Ziegler, Cai-Nicolas AU - Mcnee, Sean AU - Konstan, Joseph AU - Lausen, Georg PY - 2005/// UR - http://dx.doi.org/10.1145/1060745.1060754 ER -