Abstract
We extend a group recommender system with a case base of previous group recommendation events. We show that this offers a new way of aggregating the predicted ratings of the group members. Using user-user similarity, we align individuals from the active group with individuals from the groups in the cases. Then, using item-item similarity, we transfer the preferences of the groups in the cases over to the group that is seeking a recommendation. The advantage of a case-based approach to preference aggregation is that it does not require us to commit to a model of social behaviour, expressed in a set of formulae, that may not be valid across all groups. Rather, the CBR system’s aggregation of the predicted ratings will be a lazy and local generalization of the behaviours captured by the neighbouring cases in the case base.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bridge, D.G.: The Virtue of Reward: Performance, Reinforcement and Discovery in Case-Based Reasoning. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 1–1. Springer, Heidelberg (2005)
Golbeck, J.: Generating Predictive Movie Recommendations from Trust in Social Networks. In: Stølen, K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds.) iTrust 2006. LNCS, vol. 3986, pp. 93–104. Springer, Heidelberg (2006)
Herlocker, J.L.: Understanding and Improving Automated Collaborative Filtering Systems. PhD thesis. University of Minnesota (2000)
Jameson, A., Smyth, B.: Recommendation to Groups. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 596–627. Springer, Heidelberg (2007)
Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann (1993)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Masthoff, J.: Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and User-Adapted Interaction 14(1), 37–85 (2004)
Quijano-Sánchez, L., Bridge, D., Díaz-Agudo, B., Recio-García, J.A.: A case-based solution to the cold-start problem in group recommenders. In: Díaz Agudo, B., Watson, I. (eds.) ICCBR 2012. LNCS, vol. 7466, pp. 342–356. Springer, Heidelberg (2012)
Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B.: An architecture for developing group recommender systems enhanced by social elements. International Journal of Human-Computer Studies (in press, 2012)
Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B., Jiménez-Díaz, G.: Happy movie: A group recommender application in facebook. In: 24th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2011 (2011)
Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B., Jiménez-Díaz, G.: Social factors in group recommender systems. In: ACM-TIST, TIST-2011-01-0013 (in press, 2011)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An open architecture for collaborative filtering of netnews. In: Procs. of the Conference on Computer Supported Collaborative Work, pp. 175–186 (1994)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Schaubhut, N.A.: Technical Brief for the Thomas-Kilmann Conflict Mode Instrument. CPP Research Department (2007)
Thomas, K.W., Kilmann, R.H.: Thomas-Kilmann Conflict Mode Instrument. Tuxedo, N.Y. (1974)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Quijano-Sánchez, L., Bridge, D., Díaz-Agudo, B., Recio-García, J.A. (2012). Case-Based Aggregation of Preferences for Group Recommenders. In: Agudo, B.D., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2012. Lecture Notes in Computer Science(), vol 7466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32986-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-32986-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32985-2
Online ISBN: 978-3-642-32986-9
eBook Packages: Computer ScienceComputer Science (R0)