Trust and nuanced profile similarity in online social networks

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Trust and nuanced profile similarity in online social networks

Full text

Pdf (3.64 MB)

Source

ACM Transactions on the Web (TWEB) archive
Volume 3 , Issue 4 (September 2009) table of contents

Article No. 12

Year of Publication: 2009

ISSN:1559-1131

Author

Jennifer Golbeck

University of Maryland, College Park, College Park, MD

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 138, Downloads (12 Months): 138, Citation Count: 0

Additional Information:

abstract references index terms

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1594173.1594174 What is a DOI?

ABSTRACT

Online social networks, where users maintain lists of friends and express their preferences for items like movies, music, or books, are very popular. The Web-based nature of this information makes it ideal for use in a variety of intelligent systems that can take advantage of the users' social and personal data. For those systems to be effective, however, it is important to understand the relationship between social and personal preferences. In this work we investigate features of profile similarity and how those relate to the way users determine trust. Through a controlled study, we isolate several profile features beyond overall similarity that affect how much subjects trust hypothetical users. We then use data from FilmTrust, a real social network where users rate movies, and show that the profile features discovered in the experiment allow us to more accurately predict trust than when using only overall similarity. In this article, we present these experimental results and discuss the potential implications for using trust in user interfaces.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Barabasi, A.-L. and Albert, R. 1999. Emergence of scaling in random networks. Science 286, 509.
	2	Basu, C., Hirsh, H., and Cohen, W. 1998. Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the 15th National/10th Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence (AAAI'98/IAAI'98). American Association for Artificial Intelligence, Menlo Park, CA, 714--720.
	3	Breese, J. S., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence. 43--52.
	4	Burgess, E. and Wallin, P. 1943. Homogamy in social characteristics. Amer. J. Sociol. 2, 49, 109--124.
	5	Byrne, D. 1961. Interpersonal attraction and attitude similarity. J. Abnor. Soc. Psych. 62, 713--715.
	6	Byrne, D. 1971. The Attraction Paradigm. Academic Press, New York, NY.
	7	Castelfranchi, C. and Falcone, R. 1998. Principles of trust for mas: Cognitive anatomy, social importance, and quantification. In Proceedings of the 3rd International Conference on Multi Agent Systems.
	8	Castelfranchi, C. and Falcone, R. 2002. Social trust: A cognitive approach. In Trust and Deception in Virtual Societies. Cristano Castelfranchi and Yao-Hua Tan, Eds., Kluwer Academic Publishers.
	9	Chien, Y.-H. and George, E. 1999. A Bayesian model for collaborative filtering. In Proceedings of the 7th International Workshop on Artificial Intelligence and Statistics.
	10	Deutsch, M. 1962. Cooperation and trust. Some theoretical notes M. R. Jones, Ed. Nebraska Symposium on Motivation. Nebraska University Press.
	11	Garden, M. and Dudek, G. 2005. Semantic feedback for hybrid recommendations in recommendz. In Proceedings of the IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05). IEEE Computer Society, Los Alamitos, CA, 754--759.
	12	George, T. and Merugu, S. 2005. A scalable collaborative filtering framework based on co-clustering. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM'05). IEEE Computer Society, Los Alamitos, CA, 625--628.
	13	Golbeck, J. 2005. Computing and applying trust in Web-based social networks. Ph.D. thesis, University of Maryland, College Park, MD.
	14	Golbeck, J. 2006a. The dynamics of web-based social networks: Membership, relationships, and change. ACM Trans. Inform. Stud.
	15	Golbeck, J. 2006b. Generating predictive movie recommendations from trust in social networks. In Proceedings of the 4th International Conference on Trust Management.
	16	Golbeck, J. 2007. The dynamics of Web-based social networks: Membership, relationships, and change. First Monday 12, 11.
	17	Golbeck, J. and Hendler, J. 2006. Filmtrust: Movie recommendations using trust in Web-based social networks. In Proceedings of the IEEE Consumer Communications and Networking Conference.
	18	Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Comm. ACM 35, 12, 61--70.
	19	Golebmiewski, R. and McConike, M. 1975. The centrality of interpersonal in group processes. In Theories of Group Processes. Cary Cooper, Ed. Wiley, Hoboken, NJ.
	20	Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99). ACM, New York, NY, 230--237.
	21	Herlocker, J. L., Konstan, J. A., and Riedl, J. 2000. Explaining collaborative filtering recommendations. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW'00). ACM, New York, NY, 241--250.
	22	Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. 2004. Evaluating collaborative filtering recommender systems. ACM Trans. Inform. Syst. 22, 1, 5--53.
	23	Hofmann, T. 2004. Latent semantic models for collaborative filtering. ACM Trans. Inform. Syst. 22, 1, 89--115.
	24	Huang, Z., Chen, H., and Zeng, D. 2004. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inform. Syst. 22, 1, 116--142.
	25	Jin, R., Chai, J. Y., and Si, L. 2004. An automatic weighting scheme for collaborative filtering. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'04). ACM, New York, NY, 337--344.
	26	Kamvar, S. D., Schlosser, M. T., and Garcia-Molina, H. 2004. The eigentrust algorithm for reputation management in p2p networks. In Proceedings of the 12th International World Wide Web Conference.
	27	Katz, Y. and Golbeck, J. 2006. Social network-based trust in prioritized default logic. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06).
	28	Kleinberg, J. 2000. The small-world phenomenon: An algorithm perspective. In Proceedings of the 32nd Annual ACM Symposium on Theory of Computing (STOC'00). ACM, New York, NY, 163--170.
	29	Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. 1997a. Grouplens: Applying collaborative filtering to usenet news. Comm. ACM 40, 3, 77--87.
	30	Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. 1997b. Grouplens: Applying collaborative filtering to usenet news. Comm. ACM 40, 3, 77--87.
	31	Levin, R. and Aiken, A. 1998. Attack resistant trust metrics for public key certification. In Proceedings of the 7th USENIX Security Symposium.
	32	Ma, H., King, I., and Lyu, M. R. 2007. Effective missing data prediction for collaborative filtering. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). ACM, New York, NY, 39--46.
	33	Marsh, S. 1994. Formalising trust as a computational concept. Ph.D. thesis, Department of Mathematics and Computer Science, University of Stirling.
	34	Massa, P. and Bhattacharjee, B. 2004. Using trust in recommender systems: An experimental analysis. In Proceedings of the 2nd International Conference on Trust Management, C. Jensen, S. Poslad, and T. Dimitrakos, Eds. Lecture Notes in Computer Science, vol. 2995. Springer-Verlag.
	35	Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., and Riedl, J. 2003. Movielens unplugged: Experiences with an occasionally connected recommender system. In Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI'03). ACM, New York, NY, 263--266.
	36	Newcomb, T. 1961. The Acquaintance Process. Holt, Rinehart, and Winston, New York, NY.
	37	O'Donovan, J. and Smyth, B. 2005. Trust in recommender systems. In Proceedings of the 10th International Conference on Intelligent User Interfaces.
	38	Perny, P. and Zucker, J. D. 2001. Preference-based search and machine learning for collaborative filtering: The “film-conseil” recommender system. Inform. Interac. Intell. 1, 1, 9--48.
	39	Page, L., Brin, S., Motwani, R., and Winograd, T. 1998. The pagerank citation ranking: Bringing order to the Web. Tech. rep. 1998, Stanford University.
	40	Richardson, M., Agrawal, R., and Domingos, P. 2003. Trust management for the semantic web. In Proceedings of the 2nd International Semantic Web Conference.
	41	Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (WWW'01). ACM, New York, NY, 285--295.
	42	Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. 2000. Application of dimensionality reduction in recommender systems: A case study. In Proceedings of the ACM WebKDD Workshop.
	43	Sinha, R. and Swearingen, K. 2001. Comparing recommendations made by online systems and friends. In Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries.
	44	Srebro, N. and Jaakkola, T. 2003. Weighted low rank approximation. In Proceedings of the 20th International Conference on Machine Learning.
	45	Swearingen, K. and Sinha, R. 2001. Beyond algorithms: An hci perspective on recommender systems. In Proceedings of the ACM SIGIR Workshop on Recommender Systems.
	46	Sztompka, P. 1999. Trust: A Sociological Theory. Cambridge University Press, Cambridge, UK.
	47	Ungar, L. and Foster, D. 1998. Clustering methods for collaborative filtering. In Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park, CA.
	48	Wang, J., de Vries, A. P., and Reinders, M. J. T. 2006. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'06). ACM, New York, NY, 501--508.
	49	Watts, D. J. 2003. Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton Studies in Complexity). Princeton University Press.
	50	Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., and Chen, Z. 2005. Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'05). ACM, New York, NY, 114--121.
	51	Ziegler, C.-N. 2005. Towards decentralized recommender systems. Ph.D. thesis, Albert-Ludwigs-Universität Freiburg, Freiburg i.Br., Germany.
	52	Ziegler, C.-N. and Golbeck, J. 2006. Investigating Correlations of Trust and Interest Similarity. Decision Support Services.
	53	Ziegler, C.-N. and Lausen, G. 2004. Spreading activation models for trust propagation. In Proceedings of the IEEE International Conference on e-Technology, e-Commerce, and e-Service. IEEE Computer Society Press, Los Alamitos, CA.