Abstract
The problem of building Recommender Systems has attracted considerable attention in recent years, but most recommender systems are designed for recommending items for individuals. In this paper we develop a content based group recommender system that can recommend TV shows to a group of users. We propose a method that uses decision list rule learner (DLRL) based on Ripper to learn the rule base from user viewing history and a method called RTL strategy based on social choice theory strategies to generate group ratings. We compare our learning algorithm with the existing C4.5 rule learner and the experimental results show that the performance of our rule learner is better in terms of literals learned (size of the rule set) and our rule learner takes time that is linear to the number of training examples.
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References
Chen, Y.L., Cheng, L.-C., Chuang, C.N.: A group recommendation system with consideration of interactions among group members. Expert Syst. Appl. 34(3), 2082–2090 (2008)
Cohen, W.W.: Fast effective rule induction. In: ICML, pp. 115–123 (1995)
Cole, J.J., Gray, M.J., Lloyd, J.W., Ng, K.S.: Personalisation for user agents. In: AAMAS 2005, Utrecht, The Netherlands, pp. 603–610 (2005)
de Campos, L.M., Luna, J.M., Huete, J.F., Morales, M.A.: Group recommending: A methodological approach based on bayesian networks. In: ICDE Workshops, pp. 835–844 (2007)
Fürnkranz, J.: Separate-and-conquer rule learning. Artificial Intelligence Review 13(1), 3–54 (1999)
Gutta, S., Kurapati, K., Lee, K.P., Martino, J., Milanski, J., Schaffer, J., Zimmerman, J.: Tv content recommender system. In: AAAI/IAAI, Austin, USA, July 30- August 3, pp. 1121–1122 (2000)
Hogg, L., Jennings, N.R.: Variable Sociability in Agent-Based Decision Making. In: Jennings, N.R. (ed.) ATAL 1999. LNCS, vol. 1757, pp. 305–318. Springer, Heidelberg (2000)
Masthoff, J.: Group modeling: Selecting a sequence of television items to suit a group of viewers. User Model. User-Adapt. Interact. 14(1), 37–85 (2004)
McCarthy, J.F., Anagnost, T.D.: Musicfx: An arbiter of group preferences for computer aupported collaborative workouts. In: CSCW, pp. 363–372 (1998)
McCarthy, K., Salamó, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P.: Group recommender systems: a critiquing based approach. In: International Conference on Intelligent User Interfaces, Australia, January 29- February 1, pp. 267–269 (2006)
Mitchell, T.M.: Machine Learning. McGraw Hill, USA (1997)
O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: Polylens: A recommender system for groups of user. In: ECSCW, Germany, pp. 199–218 (September 2001)
Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)
Rivest, R.L.: Learning decision lists. Machine Learning 2(3), 229–246 (1987)
Tubio, R., Sotelo, R., Blanco, Y., Lopez, M., Gil, A., Pazos, J., Ramos, M.: A tv-anytime metadata approach to tv program recommendation for groups. In: Consumer Electronics, ISCE, pp. 1–3 (April 2008)
Yu, Z., Zhou, X., Hao, Y., Gu, J.: Tv program recommendation for multiple viewers based on user profile merging. User Model. User-Adapt. Interact. 16(1), 63–82 (2006)
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Padmanabhan, V., Seemala, S.K., Bhukya, W.N. (2011). A Rule Based Approach to Group Recommender Systems. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2011. Lecture Notes in Computer Science(), vol 7080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_3
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DOI: https://doi.org/10.1007/978-3-642-25725-4_3
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