Abstract
In an era of overwhelming choices, recommender systems aim at recommending the most suitable items to the user. Preference handling is one of the core issues in the design of recommender systems and so it is important for them to catch and model the user’s preferences as accurately as possible. In previous work, comparative preferences-based patterns were developed to handle preferences deduced by the system. These patterns assume there are only two values for each feature. However, real-world features can be multi-valued. In this paper, we develop preference induction methods which aim at capturing several preference nuances from the user feedback when features have more than two values. We prove the efficiency of the proposed methods through an experimental study.
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References
Zaslow, J.: If tivo thinks you are gay, here’s how to set it straight. The Wall Street Journal (2002)
Fishburn, P.C.: Lexicographic orders, utilities, and decision rules: A survey. Management Science 20(11), 1442–1471 (1974)
Öztürk, M., Tsoukià s, A., Vincke, P.: Preference modelling. In: Bosi, G., Brafman, R.I., Chomicki, J., Kießling, W. (eds.) Preferences. Dagstuhl Seminar Proceedings, vol. 04271, IBFI, Schloss Dagstuhl (2004)
Stefanidis, K., Koutrika, G., Pitoura, E.: A survey on representation, composition and application of preferences in database systems. ACM Transactions on Database Systems 36(3), 19 (2011)
Bridge, D.G., Göker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. The Knowledge Engineering Review 20(3), 315–320 (2005)
Chen, L., Pu, P.: Survey of preference elicitation methods. In: Technical Report IC/200467 (2004)
McGinty, L., Reilly, J.: On the evolution of critiquing recommenders. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 419–453. Springer (2011)
McGinty, L., Smyth, B.: Comparison-based recommendation. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 575–589. Springer, Heidelberg (2002)
Bridge, D.G., Ricci, F.: Supporting product selection with query editing recommendations. In: Konstan, J.A., Riedl, J., Smyth, B. (eds.) RecSys, pp. 65–72. ACM (2007)
Trabelsi, W., Wilson, N., Bridge, D.G., Ricci, F.: Preference dominance reasoning for conversational recommender systems: a comparison between a comparative preferences and a sum of weights approach. International Journal on Artificial Intelligence Tools 20(4), 591–616 (2011)
Wilson, N.: Efficient inference for expressive comparative preference languages. In: Boutilier, C. (ed.) IJCAI, pp. 961–966 (2009)
Yaman, F., Walsh, T.J., Littman, M.L., desJardins, M.: Democratic approximation of lexicographic preference models. Artificial Intelligence 175(7-8), 1290–1307 (2011)
Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Recommender Systems Handbook, pp. 257–297 (2011)
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Trabelsi, W., Wilson, N., Bridge, D. (2013). Comparative Preferences Induction Methods for Conversational Recommenders. In: Perny, P., Pirlot, M., Tsoukià s, A. (eds) Algorithmic Decision Theory. ADT 2013. Lecture Notes in Computer Science(), vol 8176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41575-3_28
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DOI: https://doi.org/10.1007/978-3-642-41575-3_28
Publisher Name: Springer, Berlin, Heidelberg
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