Difference between revisions of "2005 ImprovingRecommendationListsthr"

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* 1. Kamal Ali, Wijnand Van Stam, TiVo: Making Show Recommendations Using a Distributed Collaborative Filtering Architecture, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 22-25, 2004, Seattle, WA, USA [http://doi.acm.org/10.1145/1014052.1014097 doi:10.1145/1014052.1014097]
 
* 1. Kamal Ali, Wijnand Van Stam, TiVo: Making Show Recommendations Using a Distributed Collaborative Filtering Architecture, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 22-25, 2004, Seattle, WA, USA [http://doi.acm.org/10.1145/1014052.1014097 doi:10.1145/1014052.1014097]
* 2. Marko Balabanović, Yoav Shoham, Fab: Content-based, Collaborative Recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997 [http://doi.acm.org/10.1145/245108.245124 doi:10.1145/245108.245124]
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* 2. Marko Balabanović, [[Yoav Shoham]], Fab: Content-based, Collaborative Recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997 [http://doi.acm.org/10.1145/245108.245124 doi:10.1145/245108.245124]
 
* 3. John S. Breese, David Heckerman, Carl Kadie, Empirical Analysis of Predictive Algorithms for Collaborative Filtering, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, p.43-52, July 24-26, 1998, Madison, Wisconsin
 
* 3. John S. Breese, David Heckerman, Carl Kadie, Empirical Analysis of Predictive Algorithms for Collaborative Filtering, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, p.43-52, July 24-26, 1998, Madison, Wisconsin
 
* 4. Dan Cosley, Steve Lawrence, David M. Pennock, REFEREE: An Open Framework for Practical Testing of Recommender Systems Using ResearchIndex, Proceedings of the 28th International Conference on Very Large Data Bases, p.35-46, August 20-23, 2002, Hong Kong, China
 
* 4. Dan Cosley, Steve Lawrence, David M. Pennock, REFEREE: An Open Framework for Practical Testing of Recommender Systems Using ResearchIndex, Proceedings of the 28th International Conference on Very Large Data Bases, p.35-46, August 20-23, 2002, Hong Kong, China

Revision as of 14:19, 13 August 2019

Subject Headings: Item Diversification Task.

Notes

Cited By

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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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2005 ImprovingRecommendationListsthrCai-Nicolas Ziegler
Sean M. McNee
Joseph A. Konstan
Georg Lausen
Improving Recommendation Lists through Topic Diversification10.1145/1060745.10607542005
AuthorCai-Nicolas Ziegler +, Sean M. McNee +, Joseph A. Konstan + and Georg Lausen +
doi10.1145/1060745.1060754 +
titleImproving Recommendation Lists through Topic Diversification +
year2005 +