Abstract
Predicting people other people may like has recently become an important task in many online social networks. Traditional collaborative filtering approaches are popular in recommender systems to effectively predict user preferences for items. However, in online social networks people have a dual role as both “users” and “items”, e.g., both initiating and receiving contacts. Here the assumption of active users and passive items in traditional collaborative filtering is inapplicable. In this paper we propose a model that fully captures the bilateral role of user interactions within a social network and formulate collaborative filtering methods to enable people to people recommendation. In this model users can be similar to other users in two ways – either having similar “taste” for the users they contact, or having similar “attractiveness” for the users who contact them. We develop SocialCollab, a novel neighbour-based collaborative filtering algorithm to predict, for a given user, other users they may like to contact, based on user similarity in terms of both attractiveness and taste. In social networks this goes beyond traditional, merely taste-based, collaborative filtering for item selection. Evaluation of the proposed recommender system on datasets from a commercial online social network show improvements over traditional collaborative filtering.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Getoor, L., Sahami, M.: Using Probabilistic Relational Models for Collaborative Filtering. In: Working Notes of the KDD 1999 Workshop on Web Usage Analysis and User Profiling (1999)
Hofmann, T.: Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 259–266 (2003)
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)
Linden, G., Smith, B., York, J.: Amazon. com Recommendations: Item-to-Item Collaborative Filtering. IEEE Transactions on Internet Computing 7(1), 76–80 (2003)
Pavlov, D., Pennock, D.M.: A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains. In: Neural Information Processing Systems, pp. 1441–1448 (2002)
Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 1995), pp. 210–217 (1995)
Su, X., Khoshgoftaar, T.: A Survey of Collaborative Filtering Techniques. In: Advances in Artificial Intelligence 2009 (2009)
Ungar, L., Foster, D.: Clustering Methods for Collaborative Filtering. In: Proceedings of the AAAI 1998 Workshop on Recommender Systems, pp. 112–125 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cai, X. et al. (2010). Collaborative Filtering for People to People Recommendation in Social Networks. In: Li, J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science(), vol 6464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17432-2_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-17432-2_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17431-5
Online ISBN: 978-3-642-17432-2
eBook Packages: Computer ScienceComputer Science (R0)