Abstract
A group recommender system is designed for contexts in which more than a person is involved in the recommendation process. There are types of content (like movies) for which it would be advisable to recommend an item only if it has not yet been consumed by most of the group. In fact, it would be trivial and not significant to recommended an item if a great part of the group has already expressed a preference for it. This paper studies the impact of content novelty on the accuracy of a group recommender system, by introducing a constraint on the percentage of a group for which the recommended content has to be novel. A comparative analysis in terms of different values of the percentage of the group and for groups of different sizes, was validated through statistical tests, in order to evaluate when the difference in the accuracy values is significant. Experimental results, deeply analyzed and discussed, show that the recommendation of novel content significantly affects the performances only for small groups and only when content has to be novel for the majority of it.
This work is partially funded by Regione Sardegna under project SocialGlue, through PIA - Pacchetti Integrati di Agevolazione “Industria Artigianato e Servizi” (annualità 2010).
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
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35. Springer, Berlin (2011)
Boratto, L., Carta, S.: State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Soro, A., Vargiu, E., Armano, G., Paddeu, G. (eds.) Information Retrieval and Mining in Distributed Environments. SCI, vol. 324, pp. 1–20. Springer, Heidelberg (2011)
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 recommender systems: Combining individual models. In: Recommender Systems Handbook, pp. 677–702. Springer (2011)
Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Ellis, A., Hagino, T. (eds.) Proceedings of the 14th international conference on World Wide Web, WWW 2005, Chiba, Japan, May 10-14, pp. 22–32. ACM (2005)
Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer (2011)
O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: Polylens: A recommender system for groups of user. In: Proceedings of the Seventh European Conference on Computer Supported Cooperative Work, pp. 199–218. Kluwer (2001)
McCarthy, J.F., Anagnost, T.D.: Musicfx: An arbiter of group preferences for computer supported collaborative workouts. In: Poltrock, S.E., Grudin, J. (eds.) Proceedings of the ACM 1998 Conference on Computer Supported Cooperative Work, CSCW 1998, Seattle, WA, USA, November 14-18, pp. 363–372. ACM (1998)
Crossen, A., Budzik, J., Hammond, K.J.: Flytrap: intelligent group music recommendation. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, IUI 2002, pp. 184–185. ACM, New York (2002)
Zhiwen, Y., Xingshe, Z., Daqing, Z.: An adaptive in-vehicle multimedia recommender for group users. In: Proceedings of the 61st Semiannual Vehicular Technology Conference, vol. 5, pp. 2800–2804 (2005)
Goren-Bar, D., Glinansky, O.: Fit-recommend ing tv programs to family members. Computers & Graphics 28(2), 149–156 (2004)
Yu, Z., Zhou, X., Hao, Y., Gu, J.: Tv program recommendation for multiple viewers based on user profile merging. User Modeling and User-Adapted Interaction 16(1), 63–82 (2006)
McCarthy, K., Salamó, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P.: Cats: A synchronous approach to collaborative group recommendation. In: Sutcliffe, G., Goebel, R. (eds.) Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, Melbourne Beach, Florida, USA, May 11-13, pp. 86–91. AAAI Press (2006)
McCarthy, J.: Pocket RestaurantFinder: A situated recommender system for groups. In: Workshop on Mobile Ad-Hoc Communication at the 2002 ACM Conference on Human Factors in Computer Systems (2002)
Jameson, A.: More than the sum of its members: challenges for group recommender systems. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 48–54. ACM Press (2004)
Chen, Y., Pu, P.: Cofeel: Using emotions to enhance social interaction in group recommender systems. In: 2013 Workshop on Tools and Technology for Emotion-Awareness in Computer Mediated Collaboration and Learning, Alpine Rendez-Vous (ARV) (2013)
Jung, J.J.: Attribute selection-based recommendation framework for short-head user group: An empirical study by movielens and imdb. Expert Systems with Applications 39(4), 4049–4054 (2012)
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)
Boratto, L., Carta, S.: Exploring the ratings prediction task in a group recommender system that automatically detects groups. In: The Third International Conference on Advances in Information Mining and Management, IMMM 2013, pp. 36–43 (2013)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1999, pp. 230–237. ACM, New York (1999)
Boratto, L., Carta, S.: Using collaborative filtering to overcome the curse of dimensionality when clustering users in a group recommender system. In: Proceedings of 16th International Conference on Enterprise Information Systems (ICEIS), pp. 564–572 (2014)
Amatriain, X., Jaimes, A., Oliver, N., Pujol, J.M.: Data mining methods for recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 39–71. Springer, Boston (2011)
Boratto, L., Carta, S., Chessa, A., Agelli, M., Clemente, M.L.: Group recommendation with automatic identification of users communities. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2009, vol. 03, pp. 547–550. IEEE Computer Society, Washington, DC (2009)
Boratto, L., Carta, S., Satta, M.: Groups identification and individual recommendations in group recommendation algorithms. In: Picault, J., Kostadinov, D., Castells, P., Jaimes, A. (eds.) Practical Use of Recommender Systems, Algorithms and Technologies. CEUR Workshop Proceedings, vol. 676 (November 2010)
Boratto, L., Carta, S.: Modeling the preferences of a group of users detected by clustering: A group recommendation case-study. In: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS 2014), pp. 16:1–16:7. ACM, New York (2014)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
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
Boratto, L., Carta, S. (2014). Impact of Content Novelty on the Accuracy of a Group Recommender System. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2014. Lecture Notes in Computer Science, vol 8646. Springer, Cham. https://doi.org/10.1007/978-3-319-10160-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-10160-6_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10159-0
Online ISBN: 978-3-319-10160-6
eBook Packages: Computer ScienceComputer Science (R0)