Cai-Nicolas Ziegler
Joseph A. Konstan
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.
- Ali, K., and van Stam, W. TiVo: Making show recommendations using a distributed collaborative filtering architecture. In Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Seattle, WA, USA, 2004), ACM Press, pp. 394--401. Google Scholar
Digital Library
- Balabanović, M., and Shoham, Y. Fab - content-based, collaborative recommendation. Communications of the ACM 40, 3 (March 1997), 66--72. Google ScholarDigital Library
- Breese, J., Heckerman, D., and Kadie, C. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (Madison, WI, USA, July 1998), Morgan Kaufmann, pp. 43--52. Google ScholarDigital Library
- Cosley, D., Lawrence, S., and Pennock, D. REFEREE: An open framework for practical testing of recommender systems using ResearchIndex. In 28th International Conference on Very Large Databases (Hong Kong, China, August 2002), Morgan Kaufmann, pp. 35--46. Google ScholarDigital Library
- Deshpande, M., and Karypis, G. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems 22, 1 (2004), 143--177. Google ScholarDigital Library
- Dwork, C., Kumar, R., Naor, M., and Sivakumar, D. Rank aggregation methods for the Web. In Proceedings of the Tenth International Conference on World Wide Web (Hong Kong, China, 2001), ACM Press, pp. 613--622. Google ScholarDigital Library
- Fagin, R., Kumar, R., and Sivakumar, D. Comparing top-k lists. In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms (Baltimore, MD, USA, 2003), SIAM, pp. 28--36. Google ScholarDigital Library
- Goldberg, D., Nichols, D., Oki, B., and Terry, D. Using collaborative filtering to weave an information tapestry. Communications of the ACM 35, 12 (1992), 61--70. Google ScholarDigital Library
- Good, N., Schafer, B., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., and Riedl, J. Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the 16th National Conference on Artificial Intelligence and Innovative Applications of Artificial Intelligence (Orlando, FL, USA, 1999), American Association for Artificial Intelligence, pp. 439--446. Google ScholarDigital Library
- Hayes, C., Massa, P., Avesani, P., and Cunningham, P. An online evaluation framework for recommender systems. In Workshop on Personalization and Recommendation in E-Commerce (Malaga, Spain, May 2002), Springer-Verlag.Google Scholar
- Herlocker, J., Konstan, J., Borchers, A., and Riedl, J. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Berkeley, CA, USA, 1999), ACM Press, pp. 230--237. Google ScholarDigital Library
- Herlocker, J., Konstan, J., Terveen, L., and Riedl, J. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22, 1 (2004), 5--53. Google ScholarDigital Library
- Karypis, G. Evaluation of item-based top-N recommendation algorithms. In Proceedings of the Tenth ACM CIKM International Conference on Information and Knowledge Management (Atlanta, GA, USA, 2001), ACM Press, pp. 247--254. Google ScholarDigital Library
- Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., and Riedl, J. GroupLens: Applying collaborative filtering to usenet news. Communications of the ACM 40, 3 (1997), 77--87. Google ScholarDigital Library
- Kummamuru, K., Lotlikar, R., Roy, S., Singal, K., and Krishnapuram, R. A hierarchical monothetic document clustering algorithm for summarization and browsing search results. In Proceedings of the Thirteenth International Conference on World Wide Web (New York, NY, USA, 2004), ACM Press, pp. 658--665. Google ScholarDigital Library
- Linden, G., Smith, B., and York, J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 4, 1 (January 2003). Google ScholarDigital Library
- McLaughlin, M., and Herlocker, J. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Sheffield, UK, 2004), ACM Press, pp. 329--336. Google ScholarDigital Library
- Melville, P., Mooney, R., and Nagarajan, R. Content-boosted collaborative filtering for improved recommendations. In Eighteenth National Conference on Artificial Intelligence (Edmonton, Canada, 2002), American Association for Artificial Intelligence, pp. 187--192. Google ScholarDigital Library
- Middleton, S., Shadbolt, N., and De Roure, D. Ontological user profiling in recommender systems. ACM Transactions on Information Systems 22, 1 (2004), 54--88. Google ScholarDigital Library
- Nichols, D. Implicit rating and filtering. In Proceedings of the Fifth DELOS Workshop on Filtering and Collaborative Filtering (Budapest, Hungary, 1998), ERCIM, pp. 31--36.Google Scholar
- Oztekin, U., Karypis, G., and Kumar, V. Expert agreement and content-based reranking in a meta search environment using Mearf. In Proceedings of the Eleventh International Conference on World Wide Web (Honolulu, HW, USA, 2002), ACM Press, pp. 333--344. Google ScholarDigital Library
- Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., and Riedl, J. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work (Chapel Hill, NC, USA, 1994), ACM, pp. 175--186. Google ScholarDigital Library
- Resnick, P., and Varian, H. Recommender systems. Communications of the ACM 40, 3 (1997), 56--58. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM Conference on Electronic Commerce (Minneapolis, MN, USA, 2000), ACM Press, pp. 158--167. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. Application of dimensionality reduction in recommender systems. In ACM WebKDD Workshop (Boston, MA, USA, August 2000).Google ScholarCross Ref
- Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. Item-based collaborative filtering recommendation algorithms. In Proceedings of the Tenth International World Wide Web Conference (Hong Kong, China, May 2001). Google ScholarDigital Library
- Schafer, B., Konstan, J., and Riedl, J. Meta-recommendation systems: User-controlled integration of diverse recommendations. In Proceedings of the 2002 International ACM CIKM Conference on Information and Knowledge Management (2002), ACM Press, pp. 43--51. Google ScholarDigital Library
- Schein, A., Popescul, A., Ungar, L., and Pennock, D. Methods and metrics for cold-start recommendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Tampere, Finland, 2002), ACM Press, pp. 253--260. Google ScholarDigital Library
- Shardanand, U., and Maes, P. Social information filtering: Algorithms for automating "word of mouth". In Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (Denver, CO, USA, May 1995), ACM Press, pp. 210--217. Google ScholarDigital Library
- Spillman, W., and Lang, E. The Law of Diminishing Returns. World Book Company, Yonkers-on-Hudson, NY, USA, 1924.Google Scholar
- Tombs, M. Osmotic Pressure of Biological Macromolecules. Oxford University Press, New York, NY, USA, 1997.Google Scholar
- Vogt, C., and Cottrell, G. Fusion via a linear combination of scores. Information Retrieval 1, 3 (1999), 151--173. Google ScholarDigital Library
- Ziegler, C.-N., Lausen, G., and Schmidt-Thieme, L. Taxonomy-driven computation of product recommendations. In Proceedings of the 2004 ACM CIKM Conference on Information and Knowledge Management (Washington, D.C., USA, November 2004), ACM Press, pp. 406--415. Google ScholarDigital Library
- Ziegler, C.-N., Schmidt-Thieme, L., and Lausen, G. Exploiting semantic product descriptions for recommender systems. In Proceedings of the 2nd ACM SIGIR Semantic Web and Information Retrieval Workshop 2004 (Sheffield, UK, July 2004).Google Scholar
Index Terms
- Improving recommendation lists through topic diversification
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