Abstract
Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and opportunities for social trust prediction. In this paper, we investigate social trust prediction by exploiting temporal dynamics. In particular, we model the dynamics of user preferences in two principled ways. The first one focuses on temporal weight; the second one targets temporal smoothness. By incorporating these two types of temporal dynamics into traditional matrix factorization based social trust prediction model, two extended social trust prediction models are proposed and the corresponding algorithms to solve the models are designed too. We conduct experiments on a real-world dataset and the results demonstrate the effectiveness of our proposed new models. Further experiments are also conducted to understand the importance of temporal dynamics in social trust prediction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Jøsang A, Ismail R, Boyd C. A survey of trust and reputation systems for online service provision[J]. Decision Support Systems, 2007, 43(2): 618–644.
Lu Y, Tsaparas P, Ntoulas A, et al. Exploiting social context for review quality prediction[C]//Proceedings of the 19th International Conference on World Wide Web. New York: ACM Press, 2010: 691–700.
Golbeck J. Trust and nuanced profile similarity in online social networks[J]. ACM Transactions on the Web (TWEB), 2009, 3(4): 12.
Ma H, Zhou D, Liu C, et al. Recommender systems with social regularization[C]//Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2011: 287–296.
Richardson M, Domingos P. Mining knowledge-sharing sites for viral marketing[C]//Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2002: 61–70.
Ma N, Lim E P, Nguyen V A, et al. Trust relationship prediction using online product review data[C]//Proceedings of the 1 st ACM International Workshop on Complex Networks Meet Information & Knowledge Management. New York: ACM Press, 2009: 47–54.
Nguyen V A, Lim E P, Jiang J, et al. To trust or not to trust? predicting online trusts using trust antecedent framework[ C]//ICDM’09, Ninth IEEE International Conference on Data Minting, 2009. Washington D C: IEEE Press, 2009: 896–901.
Tavakolifard M, Almeroth K C. A taxonomy to express open challenges in trust and reputation systems[J]. Journal of Communications, 2012, 7(7): 538–551.
Jones G R, George J M. The experience and evolution of trust: Implications for cooperation and teamwork[J]. Academy of Management Review, 1998, 23(3): 531–546.
Artz D, Gil Y. A survey of trust in computer science and the semantic web[J]. Web Semantics: Science, Services and Agents on the World Wide Web, 2007, 5(2): 58–71.
Ma H, Yang H, Lyu M R, et al. Sorec: Social recommendation using probabilistic matrix factorization[C]//Proceedings of the 17th ACM Conference on Information and Knowledge Management. New York: ACM Press, 2008: 931–940.
Matsuo Y, Yamamoto H. Community gravity: Measuring bidirectional effects by trust and rating on online social net-works[C]//Proceedings of the 18th International Conference on World Wide Web. New York: ACM Press, 2009: 751–760.
Lu Y, Tsaparas P, Ntoulas A, et al. Exploiting social context for review quality prediction [C] //Proceedings of the 19th International Conference on World Wide Web. New York: ACM Press, 2010: 691–700.
Ziegler C, Golbeck J. Investigating interactions of trust and interest similarity[J]. Decision Support Systems, 2007, 43(2): 460–475.
Mishra A, Bhattacharya A. Finding the bias and prestige of nodes in networks based on trust scores[C]// Proceedings of the 20th International Conference on World Wide Web. New York: ACM Press, 2011: 567–576.
Liu H, Lim E P, Lauw H W, et al. Predicting trusts among users of online communities: An epinions case study[C] //Proceedings of the 9th ACM Conference on Electronic Commerce. New York: ACM Press, 2008: 310–319.
Leskovec J, Huttenlocher D, Kleinberg J. Predicting positive and negative links in online social networks[C]//Proceedings of the 19th International Conference on World Wide Web. New York: ACM Press, 2010:641–650.
Guha R, Kumar R, Raghavan P, et al. Propagation of trust and distrust[C]//Proceedings of the 13th International Conference on World Wide Web. New York: ACM Press, 2004: 403–412.
Menon A K, Elkan C. Link prediction via matrix factorization[ M]//Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg: Springer-Verlag, 2011:437–452.
Koren Y. Collaborative filtering with temporal dynamics[J]. Commun ACM, 2010, 53(4):89–97.
Dunlavy D M, Kolda T G, Acar E. Temporal link prediction using matrix and tensor factorizations[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2011, 5(2): 10.
Gao S, Denoyer L, Gallinari P. Temporal link prediction by integrating content and structure information[C] //Proceedings of the 20th ACM International Conference on Information and Knowledge Management. New York: ACM Press, 2011: 1169–1174.
Tang J, Gao H, Liu H, et al. eTrust: Understanding trust evolution in an online world[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2012: 253–261.
Tang J, Gao H, Liu H. mTrust: discerning multi-faceted trust in a connected world[C]//Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2012: 93–102.
Tang J, Gao H, Hu X, et al. Exploiting homophily effect for trust prediction[C]//Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2013: 53–62.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Supported by the National Natural Science Foundation of China (61063039) and Project of Guangxi Key Laboratory of Trusted Software (kx201202)
Biography: CAI Guoyong, male, Professor, research direction: social computing, trust computing.
Rights and permissions
About this article
Cite this article
Cai, G., Lv, R., Tang, J. et al. Temporal dynamics in social trust prediction. Wuhan Univ. J. Nat. Sci. 19, 369–378 (2014). https://doi.org/10.1007/s11859-014-1027-z
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11859-014-1027-z