Abstract
With the wide application of all kinds of social network services, social recommendation has attracted many attentions from academia and industry. Different from traditional recommender systems, social influence plays a significant role in social recommendation. However, many existing approaches cannot effectively simulate the influence propagation and the computation of social influence is complex. This leads to the low prediction accuracy. Hence, this paper proposes an adaptive social influence propagation model to address this problem. Moreover, we present a simple and fast social influence computation method according to the local network topology, which can provide distinguishing influences for one user depending on its neighbors. To demonstrate the performance, we design the shortest path with maximum propagation strategy and experiments are conducted to compare our model with other social influence propagation approaches on the real data set. Empirical results show that both the quality of prediction and coverage have remarkable improvement, especially with few ratings.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Wikipedia, http://zh.wikipedia.org/
Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40, 56–58 (1997)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. Internet Computing 7, 76–80 (2003)
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, pp. 175–186. ACM (1994)
Dong, W., Tao, Z., Cimini, G., Pei, W., Weiping, L., Yicheng, Y.: Effective mechanism for social recommendation of news. Physica A: Statistical Mechanics and its Applications 390, 2117–2126 (2011)
Sigrubjörnsson, B., Van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proceedings of the International Conference on World Wide Web, pp. 327–336 (2008)
Shulong, T., Jiajun, B., Chun, C., Bin, X., Can, W., Xiaofei, H.: Using rich social media information for music recommendation via hypergraph model. ACM Transactions on Multimedia Computing, Commuications, and Applications 7, 1–20 (2011)
Christakis, N.A., Fowler, J.H.: Connected. Little Brown (2010)
Yang, S.H., Long, B., Smola, A., Sadagopan, N., Zheng, Z.H., Zha, H.Y.: Like like alike: joint friendship and interest propagation in social networks. In: Proceedings of the International Conference on World Wide Web, pp. 537–546. ACM (2011)
Massa, P., Avesani, P.: Trust-aware Recommender Systems. In: Proceedings of the ACM Conference on Recommender Systems, pp. 17–24. ACM (2007)
Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the International Conference on World Wide Web, pp. 403–412. ACM (2004)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
Wu, F., Huberman, B.A., Adamic, L.A., Tyler, J.R.: Information Flow in Social Groups. Physica A: Statistical Mechanics and its Applications 337, 327–335 (2004)
Ye, M., Liu, X.J., Lee, W.C.: Exploring Social Influence for Recommendation - A Probabilistic Generative Model Approach. arXiv preprint (2011)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report. Stanford, USA (1998)
Duo, S., Tao, Z., Jianguo, L., Runran, L., Chunxiao, J., Binghong, W.: Information filtering based on transferring similarity. Physical Review E 80, 017101 (2009)
Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: Meersman, R., Tari, Z. (eds.) CoopIS/DOA/ODBASE 2004. LNCS, vol. 3290, pp. 492–508. Springer, Heidelberg (2004)
Richters, O., Peixoto, T.P.: Trust Transitivity in Social Networks. PLOS One 6, e18384 (2011)
Golbeck, J., Parsia, B., Hendler, J.: Trust Networks on the Semantic Web. In: Klusch, M., Omicini, A., Ossowski, S., Laamanen, H. (eds.) CIA 2003. LNCS (LNAI), vol. 2782, pp. 238–249. Springer, Heidelberg (2003)
Granovetter, M.S.: The strength of weak ties. American Journal of Sociology, 1360–1380 (1973)
Scott, J.: Social network analysis. SAGE Publications Limited (2012)
Bobadilla, J., Ortega, F., Hernando, A.: A collaborative filtering similarity measure based on singularities. Information Processing & Management 48, 204–238 (2012)
Deepa, A., Kamal, K.B.: Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Systems with Applications 38, 5101–5109 (2011)
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
Liu, H., Hu, Z., Tian, H., Zhou, D. (2013). An Adaptive Social Influence Propagation Model Based on Local Network Topology. In: Huemer, C., Lops, P. (eds) E-Commerce and Web Technologies. EC-Web 2013. Lecture Notes in Business Information Processing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39878-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-39878-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39877-3
Online ISBN: 978-3-642-39878-0
eBook Packages: Computer ScienceComputer Science (R0)