Abstract
Social networks are growing in number and size, with hundreds of millions of user accounts among them. One added benefit of these networks is that they allow users to encode more information about their relationships than just stating who they know. In this work, we are particularly interested in trust relationships, and how they can be used in designing interfaces. In this paper, we present FilmTrust, a website that uses trust in web-based social networks to create predictive movie recommendations. Using the FilmTrust system as a foundation, we show that these recommendations are more accurate than other techniques when the user’s opinions about a film are divergent from the average. We discuss this technique both as an application of social network analysis, as well as how it suggests other analyses that can be performed to help improve collaborative filtering algorithms of all types.
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
Abdul-Rahman, A., Hailes, S.: Supporting trust in virtual communities. In: Proceedings of the 33rd Hawaii International Conference on System Sciences, Maui, HW, USA (2000)
Ziegler, C.-N., Lausen, G.: Analyzing correlation between trust and user similarity in online communities. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 251–265. Springer, Heidelberg (2004)
Sinha, R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries Dublin, Ireland (2001)
Swearingen, K., Sinha, R.: Beyond algorithms: An HCI perspective on recommender systems. In: Proceedings of the ACM SIGIR 2001 Workshop on Recommender Systems, New Orleans, Louisiana (2001)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM conference on Computer supported cooperative work, Philadelphia, Pennsylvania, United States, December 2000, pp. 241–250 (2000)
Garden, M., Dudek, G.: Semantic feedback for hybrid recommendations in Recommendz. In: Proceedings of the IEEE International Conference on e-Technology, e-Commerce, and e-Service (EEE 2005), Hong Kong, China (March 2005)
Perny, P., Zucker, J.D.: Preference-based Search and Machine Learning for Collaborative Filtering: the “Film-Conseil” recommender system. Information, Interaction, Intelligence 1(1), 9–48 (2001)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22(1), 5–53 (2004)
Massa, P., Avesani, P.: Trust-aware Collaborative Filtering for Recommender Systems. In: Proceedings of the International Conference on Cooperative Information Systems (CoopIS) (2004)
Massa, P., Bhattacharjee, B.: Using Trust in Recommender Systems: an Experimental Analysis. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 221–235. Springer, Heidelberg (2004)
Golbeck, J.: Computing and Applying Trust in Web-Based Social Networks, Ph.D. Dissertation, University of Maryland, College Park (2005)
Golbeck, J.: Personalizing Applications through Integration of Inferred Trust Values in Semantic Web-Based Social Networks. In: Proceedings of Semantic Network Analysis Workshop, Galway, Ireland (2005)
American Film Institute, 100 Years, 100 Movies, http://www.afi.com/tvevents/100years/movies.aspx
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Golbeck, J. (2006). Generating Predictive Movie Recommendations from Trust in Social Networks. In: Stølen, K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds) Trust Management. iTrust 2006. Lecture Notes in Computer Science, vol 3986. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11755593_8
Download citation
DOI: https://doi.org/10.1007/11755593_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34295-3
Online ISBN: 978-3-540-34297-7
eBook Packages: Computer ScienceComputer Science (R0)