Trust Inference Algorithms for Social Networks
Abstract
The exponential growth of social networks has made establishing a trusted relationship increasingly important. Recommender systems can play an important role in assessing a user’s trustworthiness. Such systems are designed to offer recommendations of trustworthiness when establishing connections among social network members, where the system rates members by inferring their degrees of trust. In this work, we developed a recommender system that provides recommendations about trusted social network members. We compared the time complexity and the accuracy of the following four adapted algorithms and a new proposed algorithm: Top Trusted Members, Target’s Reputation and Similarity, Depth First Search (DFS) Trust Propagation, Dijkstra’s Trust Propagation and Target’s Followers (new). An experiment was conducted using a dataset from Twitter. The results show that the Target’s Followers algorithm is a promising approach for making accurate recommendations, especially when the network is dense.
References
Boyd D.M., Ellison N.B., Social network sites: definition, history, and scholarship. Engineering Management Review, IEEE , vol.38, no.3, pp.16-31, Third Quarter 2010.
Twitter, [Online]. Available: http://en.wikipedia.org/wiki/Twitter.
Kwak H., Lee C., Park H., Moon S. What is Twitter, a social network or a news media? .In the Proceedings of the 19th international conference on World wide- web. North Carolina, USA, 2010.
MySpace, [Online]. Available: http://en.wikipedia.org/wiki/Myspace.
Facebook, [Online]. Available: http://en.wikipedia.org/wiki/Facebook.
Faisal M., Alsumait A. Social network privacy and trust concerns. In Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Service, pp. 416-419, Dec. 2011. Vietnam.
Aimeur E., Onana F., Better control on recommender systems. The 8th IEEE International Conference on and Enterprise Computing, pp.38, 26-29 June 2006.
Mori K. Trust-Networks in Recommender Systems. Master's Projects. Dept. Computer Science, San Jose State University, 2008.
Briggs P., Smyth B. Harnessing Trust in Social Search. in G. Amati, C Carpine to and G. Romano (Eds): ECIR 2007, LNCS 4425, 2007 (c) Springer- Verlag Berlin Heidelberg, Germany, 2007, pp. 525–532.
Wei C., Fong S. Social Network Collaborative Filtering Framework and Online Trust Factors: a Case Study on Facebook, The 5th International Conference on Digital Information Management, Thunder Bay, Canada, pp. 266-273, July 2010.
Kazienko P., Musiał K. Recommendation Framework for Online Social Networks, Advances in Web Intelligence and Data Mining, Springer, vol. 23, pp.111-120, 2006.
Sarda K., Gupta P., Mukherjee D., Padhy S., Saran H., A Distributed Trust-based Recommendation System on Social Networks. In Proceedings of the 14th ACM SIGKDD International conference on knowledge discovery and data mining, pp. 160-168, 2008.
Golbeck J., Computing and Applying Trust in Web-Based Social Networks, PhD Thesis, Dept. Computer Science, Maryland University, 2005.
Rashid A. M., Karypis G., Riedl J. Influence in Ratings-Based Recommender Systems: An Algorithm-Independent Approach. In Proceedings of SIAM International conference on Data Mining. Newport Beach, CA, USA, 2005.
Maheswari S. Empirical evaluation of reputation based trust in semantic web. In the Proceedings of International Journal of Engineering Science and Technology. vol. 2, 2010.
Noh S., Han S., A study on reliable social network metrics. International Journal of Web Services Practices, vol. 5, no.1, pp. 41- 43, 2010.
Cha M., Haddadi H., Benevenuto F., Gummadi K. Measuring User Influence in Twitter: The Million Follower Fallacy .In the Proceedings of Int’l AAAI Conference on Weblogs and Social Media (ICWSM).
Sun Y.; Yu W., Han Z.; Liu K. Trust modeling and evaluation in ad hoc networks. Global Telecommunications Conference, 2005. GLOBECOM '05.IEEE , vol.3, no., pp. 6- 28. Nov.-2 Dec. 2005.
Weng J., Miao C., Goh A. Improving Collaborative Filtering with Trust-based Metrics. In the Proceedings of the 2006 ACM symposium on Applied computing, York, NY, USA , 2006.
Resnick P., Iacovou N., Suchak M., Bergstorm P., Riedl J. Grouplens: An open architecture for collaborative filtering of net news. In Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, Chapel Hill, North Carolina, pp. 175–186, 1994.
Resnick P., Varian H. Recommender systems.
Communications of the ACM, v.40 n.3, p.56-58, March 1997
Capra L. Toward a human trust model for mobile ad-hoc networks. In: Proceedings of second UK UbiNet workshop; 2004.
Dijkstra, E. W. (1959). "A note on two problems in connexi on with graphs". Numerische Mathematik 1: pp. 269–271.
NodeXL, [Online]. Available: http://nodexl.codeplex.com/.
