Abstract
Social network based applications such as Facebook, Myspace and LinkedIn have become very popular among Internet users, and a major research problem is how to use the social network information to better infer users’ preferences and make better recommender systems. A common trend is combining the user-item rating matrix and users’ social network for recommendations. However, existing solutions add the social network information for a particular user without considering the different characteristics of the products to be recommended and the neighbors involved. This paper proposes a new approach that can adaptively utilize social network information based on the context characterized by products and users. This approach complements several existing social network based recommendation algorithms and thus can be integrated with existing solutions. Experimental results on Epinions data set demonstrate the added value of the proposed solution in two recommendation tasks: rating prediction and top-K recommendations.
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.
Similar content being viewed by others
References
Berkovsky, S., Kuflik, T., Ricci, F.: Distributed collaborative filtering with domain specialization. In: RecSys, pp. 33–40 (2007)
Gunawardana, A., Meek, C.: A unified approach to building hybrid recommender systems. In: RecSys, pp. 117–124 (2009)
Jamali, M., Ester, M.: A transitivity aware matrix factorization model for recommendation in social networks. In: IJCAI, pp. 2644–2649 (2011)
Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: CIKM, pp. 247–254 (2001)
Kautz, H.A., Selman, B., Shah, M.A.: Referral web: Combining social networks and collaborative filtering. Commun. ACM 40(3), 63–65 (1997)
Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: SIGIR, pp. 195–202 (2009)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD, pp. 426–434 (2008)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: SIGIR, pp. 203–210 (2009)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011)
Massa, P., Avesani, P.: Trust-aware recommender systems. In: RecSys, pp. 17–24 (2007)
McDonald, D.W.: Recommending collaboration with social networks: a comparative evaluation. In: CHI, pp. 593–600 (2003)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, M., Jiang, Z., Luo, B., Tang, J., Gu, Q., Chen, D. (2013). Product and User Dependent Social Network Models for Recommender Systems. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-37456-2_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37455-5
Online ISBN: 978-3-642-37456-2
eBook Packages: Computer ScienceComputer Science (R0)