Document Type : Research Article

Authors

Abstract

In Social networks, users need a proper estimation of trust in others to be able to initialize reliable relationships. Some trust evaluation mechanisms have been offered, which use direct ratings to calculate or propagate trust values. However, in some web-based social networks where users only have binary relationships, there is no direct rating available. Therefore, a new method is required to infer trust values in these networks. To bridge this gap, this paper aims to propose a new method which takes advantage of user similarity to predict trust values without any need for direct ratings. In this approach, which is based on socio-psychological studies, user similarity is calculated from the profile information and the texts shared by the users via text-mining techniques. Applying Ziegler ratios to our approach revealed that users are more than 50% more similar to their trusted agents than to arbitrary peers, which proves the validity of the original idea of the study about inferring trust from language similarity. In addition, comparing the real assigned ratings, gathered directly from users, with the experimental results indicated that the predicted trust values are sufficiently acceptable (with a precision of 61%). We have also studied the benefits of using context in inferring trust. In this regard, the analysis revealed that the precision of the predictions can be improved up to 72%. Besides the application of this approach in web-based social networks, the proposed technique can also be of much help in any direct rating mechanism to evaluate the correctness of trust values assigned by users, and increase the robustness of trust and reputation mechanisms against possible security threats.

Keywords

[1] A. Josang, R. Ismail, and C. Boyd. A survey of trust and reputation systems for online service provision. Decision Support Systems, 43(2):618-644, 2007. ISSN 0167-9236.
[2] J. Golbeck. Computing and applying trust in web-based social networks. In Proceedings of Computational Intelligence and Industrial Application, PACIIA '08, pages 342{347, 2008.
[3] P. Avesani, P. Massa, and R. Tiella. A trust-enhanced recommender system application: Moleskiing. In Proceedings of the 2005 ACM Symposium on Applied Computing, SAC '05, pages 1589-1593, New York, NY, USA, 2005. ACM. ISBN 1-58113-964-0.
[4] J. Golbeck. Trust and nuanced profile similarity in online social networks. ACM Trans. Web, 3 (4):12:1-12:33, September 2009. ISSN 1559-1131.
[5] CN. Ziegler and J. Golbeck. Investigating interactions of trust and interest similarity. Decision Support Systems, 43(2):460-475, 2007.
[6] M.A. Morid, A. Omidvar, and H.R. Shahriari. An enhanced method for computation of similarity between the contexts in trust evaluation using weighted ontology. In Trust, Security and Privacy in Computing and Communications (TrustCom), 2011 IEEE 10th International Conference on, pages 721-725, 2011.
[7] Y. Wang and J. Vassileva. Trust and reputation model in peer-to-peer networks. In Peer-to-Peer Computing, pages 150-157. IEEE Computer Society, 2003. ISBN 0-7695-2023-5.
[8] D. Artz and Y. Gil. A survey of trust in computer science and the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web, 5(2):58-71, 2007. ISSN 1570-8268.
[9] L. Mui, M. Mohtashemi, and A. Halberstadt. A computational model of trust and reputation. In System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on, 2002.
[10] M. Grandison, T. Sloman. A survey of trust in internet applications. Communications Surveys Tutorials, IEEE, 3(4):2-16, 2000. ISSN 1553- 877X.
[11] D. Olmedilla, OF. Rana, B. Matthews, and W. Nejdl. Security and trust issues in semantic grids. In Proceedings of the Dagsthul Seminar, Semantic Grid: The Convergence of Technologies, volume 5271, 2005.
[12] M. Tavakolifard, SJ. Knapskog, and P. Herrmann. Trust transferability among similar contexts. In Q2SWinet, pages 91-97. ACM, 2008. ISBN 978-1-60558-237-5.
[13] R. Neisse, M. Wegdam, and M. Van Sinderen. Context-aware trust domains. In EuroSSC, volume 4272 of Lecture Notes in Computer Science, pages 234-237. Springer, 2006. ISBN 3-540-47842-6.
[14] R. Neisse, M. Wegdam, M. Sinderen, and G. Lenzini. Trust management model and architecture for context-aware service platforms. In On the Move to Meaningful Internet Systems 2007: CoopIS, DOA, ODBASE, GADA, and IS, volume 4804 of Lecture Notes in Computer Science, pages 1803-1820. Springer Berlin Heidelberg, 2007. ISBN 978-3-540-76835-7.
[15] S. Holtmanns and Z. Yan. Context-aware adaptive trust. In Developing Ambient Intelligence, pages 137-146. Springer Paris, 2006. ISBN 978-2-287-47469-9.
[16] M. Rehak, M. Gregor,M. Pechoucek, and J. Bradshaw. Representing context for multi-agent trust modeling. In IAT, pages 737-746. IEEE Computer Society, 2006.
[17] S. Toivonen and G. Denker. The impact of context on the trustworthiness of communication: An ontological approach. In ISWC Workshop on Trust, Security, and Reputation on the Semantic Web, volume 127 of CEUR Workshop Proceedings. CEUR-WS.org, 2004.
[18] M. Rehak and M. Pechoucek. Trust modeling with context representation and generalized identities. In CIA, volume 4676 of Lecture Notes in Computer Science, pages 298-312. Springer, 2007.
ISBN 978-3-540-75118-2.
[19] N. Gujral, D. DeAngelis, K. Fullam, and K. Barber. Modeling multi-dimensional trust. In Proceedings of the Workshop on Trust in Agent Societies, 2008.
[20] EW. Burgess and P. Wallin. Homogamy in social characteristics. American Journal of Sociology, 49:pp. 109-124, 1943. ISSN 00029602.
[21] T. Newcomb. The acquaintance process. Holt, Rinehart, and Winston, 1961.
[22] D. Byrne. Interpersonal attraction and attitude similarity. The Journal of Abnormal and Social Psychology, 62:pp. 713-715, 1961.
[23] D. Byrne. The attraction paradigm. Academic Press, 1971.
[24] J. Golbeck, C. Robles, M. Edmondson, and K. Turner. Predicting personality from twitter. In SocialCom/PASSAT, pages 149-156. IEEE, 2011. ISBN 978-1-4577-1931-8.
[25] D. Quercia, M. Kosinski, D. Stillwell, and J. Crowcroft. Our twitter profiles, our selves: Predicting personality with twitter. In SocialCom/PASSAT, pages 180-185. IEEE, 2011. ISBN 978-1-4577-1931-8.
[26] J. Tang, H. Gao, X. Hu, and H. Liu. Exploiting homophily effect for trust prediction. In WSDM, pages 53-62. ACM, 2013. ISBN 978-1-4503-1869-3.
[27] H. Mohammadhassanzadeh and H.R. Shahriari. Using user similarity to infer trust values in social networks regardless of direct ratings. In Information Security and Cryptology (ISCISC), 2012 9th International ISC Conference on, pages 66-72, 2012.
[28] A. Huang. Similarity measures for text document clustering. In Proceedings of the Sixth New Zealand Computer Science Research Student Conference (NZCSRSC2008), Christchurch, New Zealand, pages 49-56, 2008.
[29] M. Porter. An algorithm for suffix stripping. Program: electronic library and information systems, 14(3):130-137, 1980.
[30] A. Sharifloo and M. Shamsfard. A bottom up approach to Persian stemming. In IJCNLP, 2008.
[31] A. Strehl, J. Ghosh, and R. Mooney. Impact of similarity measures on web-page clustering. In Proceedings of the AAAI Workshop on AI for Web Search (AAAI 2000), pages 58-64, Austin, TX, USA, 2000.
[32] FG. Marmol and GM. Perez. Security Threats Scenarios in Trust and Reputation Models for Distributed Systems. Elsevier Computers and Security, 2009.