Abstract
Case-Based Reasoning has been studied as a methodology to support ratings-based collaborative recommendation, but this predominantly targets the context of an individual end-user. There are, however, many circumstances where several people participating together in a group activity could benefit from recommendations tailored to the group as a whole. Group recommendation has received comparatively little attention overall, and recent research has largely focused on making straightforward individual recommendations for each group member and then aggregating the results. But this examines only the context of the target group, and does not take advantage of other, previous group contexts as a first-class element of the knowledge base. Recent research investigated how case-based reasoning approaches can be applied to retrieve and reuse whole previous groups as a basis for recommendation and showed an advantage over traditional aggregation approaches. In this paper we focus on further exploration of the space. We present our approach for case-based group recommendation, as well as evaluation results across conditions for group size and homogeneity. Results show that foundational group-to-group approaches outperform individual-to-group recommendations across a wide range of group contexts.
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., Göker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. The Knowledge Engineering Review 20(3) (2005)
McCarthy, K., McGinty, L., Smyth, B.: Case-based group recommendation: Compromising for success. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 299–313. Springer, Heidelberg (2007)
Burke, R.: A case-based reasoning approach to collaborative filtering. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 370–379. Springer, Heidelberg (2000)
Hayes, C., Cunningham, P., Smyth, B.: A case-based reasoning view of automated collaborative filtering. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 234–248. Springer, Heidelberg (2001)
O’Sullivan, D., Wilson, D., Smyth, B.: Using collaborative filtering data in case-based recommendation. In: Proceedings of the 15th International FLAIRS Conference (2002)
O’Sullivan, D., Wilson, D.C., Smyth, B.: Improving case-based recommendation: A collaborative filtering approach. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 278–291. Springer, Heidelberg (2002)
Quijano-Sánchez, L., Recio-GarcÃa, J.A., DÃaz-Agudo, B., Jimenez-Diaz, G.: Social factors in group recommender systems. ACM Trans. Intell. Syst. Technol. 4(1) (2013)
O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: Polylens: A recommender system for groups of users. In: Proceedings of the Seventh European Conference on Computer Supported Cooperative Work (2001)
McCarthy, J.F.: Pocket RestaurantFinder: A situated recommender system for groups. In: Proceedings of the ACM Conference on Human Factors in Computer Systems Workshop on Mobile Ad-Hoc Communication (2002)
Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the Fourth ACM Conference on Recommender Systems (2010)
McCarthy, K., Salamó, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P.: CATS: A synchronous approach to collaborative group recommendation. In: Proceedings of the 19th International FLAIRS Conference (2006)
Sprague, D., Wu, F., Tory, M.: Music selection using the PartyVote democratic jukebox. In: Proc. of the Working Conference on Advanced Visual Interfaces (2008)
Quijano-Sánchez, L., Bridge, D., DÃaz-Agudo, B., Recio-GarcÃa, J.A.: Case-based aggregation of preferences for group recommenders. In: DÃaz Agudo, B., Watson, I. (eds.) ICCBR 2012. LNCS, vol. 7466, pp. 327–341. Springer, Heidelberg (2012)
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)
Masthoff, J.: Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and User-Adapted Interaction 14(1) (2004)
Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., Seada, K.: Enhancing group recommendation by incorporating social relationship interactions. In: Proceedings of the 16th ACM International Conference on Supporting Group Work (2010)
Recio-GarcÃa, J.A., Jimenez-Diaz, G., Sanchez-Ruiz, A.A., Diaz-Agudo, B.: Personality aware recommendations to groups. In: Proceedings of the Third ACM Conference on Recommender Systems (2009)
Quijano-Sánchez, L., Bridge, D., DÃaz-Agudo, B., Recio-GarcÃa, J.A.: A case-based solution to the cold-start problem in group recommenders. In: DÃaz Agudo, B., Watson, I. (eds.) ICCBR 2012. LNCS, vol. 7466, pp. 342–356. Springer, Heidelberg (2012)
McCarthy, K., McGinty, L., Smyth, B., Salamó, M.: The needs of the many: A case-based group recommender system. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 196–210. Springer, Heidelberg (2006)
Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems (2010)
Burke, R.: Hybrid recommender systems: Survey and experiments. User-Modeling and User-Adapted Interaction 12(4) (2002)
Cox, M.T., Muñoz-Avila, H., Bergmann, R.: Case-based planning. The Knowledge Engineering Review 20(3) (2005)
Spalzzi, L.: A survey on case-based planning. Artificial Intelligence Review 16(1) (2001)
Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5(4) (2002)
Resnick, 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 (1994)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1) (2004)
Salamó, M., McCarthy, K., Smyth, B.: Generating recommendations for consensus negotiation in group personalization services. Personal and Ubiquitous Computing 16(5) (2012)
Amer-Yahia, S., Roy, S.B., Chawlat, A., Das, G., Yu, C.: Group recommendation: Semantics and efficiency. Proceedings of the VLDB Endowment 2(1) (2009)
Garcia, I., Sebastia, L., Onaindia, E., Guzman, C.: A group recommender system for tourist activities. In: Di Noia, T., Buccafurri, F. (eds.) EC-Web 2009. LNCS, vol. 5692, pp. 26–37. Springer, Heidelberg (2009)
Chen, Y.L., Cheng, L.C., Chuang, C.N.: A group recommendation system with consideration of interactions among group members. Expert Syst. Appl. 34 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wilson, D.C., Najjar, N.A. (2014). Exploring the Space of Whole-Group Case Retrieval in Making Group Recommendations. In: Lamontagne, L., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 2014. Lecture Notes in Computer Science(), vol 8765. Springer, Cham. https://doi.org/10.1007/978-3-319-11209-1_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-11209-1_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11208-4
Online ISBN: 978-3-319-11209-1
eBook Packages: Computer ScienceComputer Science (R0)