2005 ImprovingRecommendationListsthr

• (Ziegler et al., 2005) ⇒ Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. (2005). “Improving Recommendation Lists through Topic Diversification.” In: Proceedings of the 14th International Conference on World Wide Web. ISBN:1-59593-046-9 doi:10.1145/1060745.1060754

Subject Headings: Item Diversification Task.

Cited By

• http://scholar.google.com/scholar?q=%222005%22+Improving+Recommendation+Lists+through+Topic+Diversification
• http://dl.acm.org/citation.cfm?id=1060745.1060754&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2005 ImprovingRecommendationListsthr	Joseph A. Konstan Cai-Nicolas Ziegler Sean M. McNee Georg Lausen			Improving Recommendation Lists through Topic Diversification				10.1145/1060745.1060754		2005