Abstract
The accuracy of collaborative filtering recommender systems largely depends on two factors: the quality of the recommendation algorithm and the nature of the available item ratings. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, not all the ratings are equally useful and therefore, in order to minimize the users’ rating effort, only some of them should be requested or acquired. In this paper we consider several rating elicitation strategies and we evaluate their system utility, i.e., how the overall behavior of the system changes when these new ratings are added. We simulate the limited knowledge of users, i.e., not all the rating requests of the system are satisfied by the users, and we compare the capability of the considered strategies in requesting ratings for items that the user experienced. We show that different strategies can improve different aspects of the recommendation quality with respect to several metrics (MAE, precision, ranking quality and coverage) and we introduce a voting-based strategy that can achieve an excellent overall performance.
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References
Carenini, G., Smith, J., Poole, D.: Towards more conversational and collaborative recommender systems. In: Proceedings of the 2003 International Conference on Intelligent User Interfaces, Miami, FL, USA, January 12-15, pp. 12–18 (2003)
Elahi, M., Ricci, F., Repsys, V.: System-wide effectiveness of active learning in collaborative filtering. In: Bonchi, F., Buntine, W., Gavalda, R., Guo, S. (eds.) Proceedings of the International Workshop on Social Web Mining, Co-located with IJCAI, Barcelona, Spain (July 2011)
Harpale, A.S., Yang, Y.: Personalized active learning for collaborative filtering. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 91–98. ACM, New York (2008)
Jin, R., Si, L.: A bayesian approach toward active learning for collaborative filtering. In: UAI 2004: Proceedings of the 20th Conference in Uncertainty in Artificial Intelligence, Banff, Canada, July 7-11, pp. 278–285 (2004)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 145–186. Springer, Heidelberg (2011)
Liu, N.N., Yang, Q.: Eigenrank: a ranking-oriented approach to collaborative filtering. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 83–90. ACM, New York (2008)
Manning, C.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
McNee, S.M., Lam, S.K., Konstan, J.A., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.) UM 2003. LNCS, vol. 2702, pp. 178–187. Springer, Heidelberg (2003)
Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., Mcnee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: Learning new user preferences in recommender systems. In: IUI 2002: Proceedings of the 2002 International Conference on Intelligent User Interfaces, pp. 127–134. ACM Press, New York (2002)
Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. SIGKDD Explorations 10(2), 90–100 (2008)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, Heidelberg (2011)
Rubens, N., Kaplan, D., Sugiyama, M.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 735–767. Springer, Heidelberg (2011)
Weimer, M., Karatzoglou, A., Smola, A.: Adaptive collaborative filtering. In: RecSys 2008: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 275–282. ACM, New York (2008)
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Elahi, M., Repsys, V., Ricci, F. (2011). Rating Elicitation Strategies for Collaborative Filtering. In: Huemer, C., Setzer, T. (eds) E-Commerce and Web Technologies. EC-Web 2011. Lecture Notes in Business Information Processing, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23014-1_14
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DOI: https://doi.org/10.1007/978-3-642-23014-1_14
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