Abstract
The installation of recommender systems in e-applications like online shops is common practice to offer alternative or cross-selling products to their customers. Usually collaborative filtering methods, like e.g. the Pearson correlation coefficient algorithm, are used to detect customers with a similar taste concerning some items. These customers serve as recommenders for other users. In this paper we introduce a novel approach for a recommender system that is based on user preferences, which may be mined from log data in a database system. Our notion of user preferences adopts a very powerful preference model from database systems. An evaluation of our prototype system suggests that our prediction quality can compete with the widely-used Pearson-based approach. In addition, our approach can achieve an added value, because it yields better results when there are only a few recommenders available. As a unique feature, preference-based recommender systems can deal with multi-attribute recommendations.
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References
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. of the 14th Annual Converence on Uncertainty in Artificial Intelligence, Madison, Wisconsin, USA, pp. 43–52 (1998)
Resnik, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proceedings of the ACM, Conference on Computer Supported Cooperative Work, pp. 175–186 (1994)
Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating ‘word of mouth’. In: Proceedings of the Conference on Human Factors in Computing Systems, pp. 210–217 (1995)
Kießling, W.: Foundations of Preferences in Database Systems. In: Proceedings of the 28th International Conference on Very Large Databases (VLDB 2002), Hong Kong, China, pp. 311–322 (2002)
Chomicki, J.: Preference Formulas in Relational Queries. ACM Transactions on Database Systems (TODS) 28(4), 427–466 (2003)
Ioannidis, Y., Koutrika, G.: Personalized Systems: Models and Methods from an IR and DB Perspective. In: 31th International Conference on Very Large Databases (VLDB 2005), Tutorial, Trondheim, Norway (2005)
Spearman, C.: The proof and measurement for association between two things. American Journal of Psychology 15, 72–101 (1904)
Varol, Y., Rotem, D.: An algorithm to generate all topological sorting arrangements. Computer Journal 24, 83–84 (1981)
Brightwell, G., Winkler, P.: Counting Linear Extensions. Order 15(3), 225–242 (1904)
Karzanov, A., Khachiyan, L.: On the conductance of order markov chains. Order 8(1), 7–15 (1991)
Jerrum, M., Sinclair, A.: The markov chain monte carlo method: an approach to approximate counting and integration. PWS Publishing, Boston (1996)
Satzger, B.: Development and evaluation of a software prototype to generate preference-based recommendations (in German). Diploma thesis, Chair for Databases and Information Systems, University of Augsburg (December 2005)
Holland, S., Ester, M., Kießling, W.: Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS, vol. 2838, pp. 204–216. Springer, Heidelberg (2003)
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Satzger, B., Endres, M., Kießling, W. (2006). A Preference-Based Recommender System. In: Bauknecht, K., Pröll, B., Werthner, H. (eds) E-Commerce and Web Technologies. EC-Web 2006. Lecture Notes in Computer Science, vol 4082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823865_4
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DOI: https://doi.org/10.1007/11823865_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37743-6
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