Abstract
Collaborative filtering (CF) based recommender systems often suffer from the sparsity problem, particularly for new and inactive users when they use the system. The emerging trend of social networking sites can potentially help alleviate the sparsity problem with their provided social relationship data, by which users’ similar interests might be inferred even with few of their behavioral data with items (e.g., ratings). Previous works mainly focus on the friendship and trust relation in this respect. However, in this paper, we have in-depth explored a new kind of social relationship - the membership and its combinational effect with friendship. The social relationships are fused into the CF recommender via a graph-based framework on sparse and dense datasets as obtained from Last.fm. Our experiments have not only revealed the significant effects of the two relationships, especially the membership, in augmenting recommendation accuracy in the sparse data condition, but also identified the outperforming ability of the graph modeling in terms of realizing the optimal fusion mechanism.
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
Aggarwal, C.C., Wolf, J.L., Wu, K.-L., Yu, P.S.: Horting hatches an egg: a new graph-theoretic approach to collaborative filtering. In: KDD 1999: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 201-212. ACM, New York (1999)
Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., Aly, M.: Video suggestion and discovery for youtube: taking random walks through the view graph. In: WWW 2008: Proceeding of the 17th international conference on World Wide Web, pp. 895–904. ACM Press, New York (2008)
Baym, N.K., Ledbetter, A.: Tunes that bind?: Predicting friendship strength in a music-based social network. In: AOIR 2008: the Association of Internet Researchers, Copenhagen, Denmark (2008)
Boyd, D.: Friends, friendsters, and myspace top 8: Writing community into being on social network sites (2006)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (UAI 1998), pp. 43–52 (1998)
Doyle, P.G., Laurie, S.J.: Random walks and electrical networks (1984)
Fouss, F., Pirotte, A., Renders, J.-M., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation, pp. 355-369 (2007)
Golbeck, J.: Generating predictive movie recommendations from trust in social networks. Trust Management, 93–104 (2006)
Gori, M., Pucci, A.: Itemrank: a random-walk based scoring algorithm for recommender engines. In: IJCAI 2007: Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 2766–2771. Morgan Kaufmann Publishers Inc., San Francisco (2007)
Groh, G., Ehmig, C.: Recommendations in taste related domains: collaborative filtering vs. social filtering. In: GROUP 2007: Proceedings of the 2007 International ACM Conference on Supporting Group Work, pp. 127–136. ACM Press, New York (2007)
Guy, I., Ronen, I., Wilcox, E.: Do you know?: recommending people to invite into your social network. In: IUI 2009: Proceedings of the 13th International Conference on Intelligent User Interfaces, pp. 77–86. ACM Press, New York (2009)
Haveliwala, T.H.: Topic-sensitive pagerank. In: WWW 2002: Proceedings of the 11th International Conference on World Wide Web, pp. 517–526. ACM Press, New York (2002)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM 2008: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pp. 263–272. IEEE Computer Society, Washington, DC, USA (2008)
Huang, Z.: Graph-based analysis for e-commerce recommendation, Tucson, AZ, USA (2005)
Hummel, H.G.K., Berg, B.V.D., Berlanga, A.J., Drachsler, H., Janssen, J., Nadolski, R., Koper, R.: Combining social based and information based approaches for personalised recommendation on sequencing learning activities, pp. 152–168 (2007)
Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: KDD 2009: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM Press, New York (2009)
Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: KDD 2009: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM Press, New York (2009)
Kautz, H., Selman, B., Shah, M.: Referral web: combining social networks and collaborative filtering. ACM Commun., 63–65 (1997)
Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: SIGIR 2009: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 195–202. ACM, New York (2009)
Lam, C.: Snack: incorporating social network information in automated collaborative filtering. In: EC 2004: Proceedings of the 5th ACM Conference on Electronic Commerce, pp. 254–255. ACM, New York (2004)
Liu, N.N., Yang, Q.: Eigenrank: a ranking-oriented approach to collaborative filtering. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 83–90. ACM, New York (2008)
Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: CIKM 2008: Proceeding of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940. ACM, New York (2008)
Roth, M., Ben-David, A., Deutscher, D., Flysher, G., Horn, I., Leichtberg, A., Leiser, N., Matias, Y., Merom, R.: Suggesting friends using the implicit social graph. In: KDD 2010: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 233–242. ACM, New York (2010)
Spertus, E., Sahami, M., Buyukkokten, O.: Evaluating similarity measures: a large-scale study in the orkut social network. In: KDD 2005: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 678–684. ACM, New York (2005)
Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications. In: ICDM 2006: Proceedings of the Sixth International Conference on Data Mining, pp. 613-622. IEEE Computer Society, Washington, DC, USA (2006)
Wang, J., de Vries, A.P., Reinders: A user-item relevance model for log-based collaborative filtering, pp. 37-48 (2006)
Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long- and short-term preference fusion. In: KDD 2010: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 723–732. ACM, New York (2010)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Yuan, Q., Chen, L., Zhao, S. (2012). Augmenting Collaborative Recommenders by Fusing Social Relationships: Membership and Friendship. In: Recommender Systems for the Social Web. Intelligent Systems Reference Library, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25694-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-25694-3_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25693-6
Online ISBN: 978-3-642-25694-3
eBook Packages: EngineeringEngineering (R0)