Rubi Boim
Tova Milo
ABSTRACT
This paper considers a popular class of recommender systems that are based on Collaborative Filtering (CF) and proposes a novel technique for diversifying the recommendations that they give to users. Items are clustered based on a unique notion of priority-medoids that provides a natural balance between the need to present highly ranked items vs. highly diverse ones. Our solution estimates items diversity by comparing the rankings that different users gave to the items, thereby enabling diversification even in common scenarios where no semantic information on the items is available. It also provides a natural zoom-in mechanism to focus on items (clusters) of interest and recommending diversified similar items. We present DiRec a plug-in that implements the above concepts and allows CF Recommender systems to diversify their recommendations. We illustrate the operation of DiRec in the context of a movie recommendation system and present a thorough experimental study that demonstrates the effectiveness of our recommendation diversification technique and its superiority over previous solutions.
- Imdb interface. http://www.imdb.com/interfaces/.Google Scholar
- G. Adomavicius and A. Tuzhilin. Towards the next generation of recommender systems. IEEE TKDE, 2005. Google ScholarDigital Library
- J. Bennet and S. Lanning. The netflix prize. KDD Cup, 2007.Google Scholar
- A. Beygelzimer, S. Kakade, and J. Langford. Cover trees for nearest neighbor. ICML, 2006. Google ScholarDigital Library
- R. Boim, H. Kaplan, T. Milo, and R. Rubinfeld. Improved recommendations via (more) collaboration. WebDB, 2010. Google ScholarDigital Library
- R. Boim, T. Milo, and S. Novgorodov. Diversification and refinement in collaborative filtering recommender (full version). http://www.cs.tau.ac.il/boim/publications/cikm11-full.pdf.Google Scholar
- R. Boim, T. Milo, and S. Novgorodov. Direc: Diversified recommendations for semantic-less collaborative filtering. ICDE, 2011. Google ScholarDigital Library
- Z. Chen and T. Li. Addressing diverse user preferences in sql-query-result navigation. SIGMOD, 2007. Google ScholarDigital Library
- M. Drosou and E. Pitoura. Search result diversification. SIGMOD Record, 2010. Google ScholarDigital Library
- S. Gollapudi and A. Sharma. An axiomatic approach for result diversification. WWW, 2009. Google ScholarDigital Library
- L. Kaufman and P. Rousseeuw. Finding groups in data: An introduction to cluster analysis. Wiley's Series in Probability and Statistics, 1990.Google Scholar
- B. Liu and H. Jagadish. Using trees to depict a forest. VLDB, 2009. Google ScholarDigital Library
- J. L. Rodgers and W. A. Nicewander. Thirteen ways to look at the correlation coefficient. The American Statistician, 1988.Google ScholarCross Ref
- J. Stoyanovich, S. Amer-Yahia, and T. Milo. Making interval-based clustering rank-aware. to appear in EDBT, 2011. Google ScholarDigital Library
- X. Su and T. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009. Google ScholarDigital Library
- C. Yu, L. Lakshmanan, and S. Amer-Yahia. It takes variety to make a world: Diversification in recommender systems. EDBT, 2009. Google ScholarDigital Library
- C. Yu, L. Lakshmanan, and S. Amer-Yahia. Recommendation diversification using explanations. ICDE, 2009. Google ScholarDigital Library
- M. Zhang and N. Hurley. Avoiding monotony: Improving the diversity of recommendation lists. RecSys, 2008. Google ScholarDigital Library
- M. Zhang and N. Hurley. Evaluating the diversity of top-n recommendations. ICTAI, 2009. Google ScholarDigital Library
- C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. WWW, 2005. Google ScholarDigital Library
Index Terms
- Diversification and refinement in collaborative filtering recommender
Recommendations
Improving recommendation lists through topic diversification
WWW '05: Proceedings of the 14th international conference on World Wide WebIn 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 ...
An Adaptive Collaborative Filtering Algorithm Based on Multiple Features
ADMA 2013: Part II of the Proceedings of the 9th International Conference on Advanced Data Mining and Applications - Volume 8347Due to the rapid development of E-commerce, personalized recommendations have been indispensable. The conventional user-based collaborative filtering CF cannot well satisfy users' requirements, besides the recommendation results are not accurate enough. ...
New approaches to mood-based hybrid collaborative filtering
CAMRa '10: Proceedings of the Workshop on Context-Aware Movie RecommendationRecently, mood has proved to be an important contextual feature in context-aware recommender systems (CARS) by some studies. In this paper we propose two new approaches to mood-based hybrid collaborative filtering (CF) in order to further improve the ...
Comments