Generating Predictive Movie Recommendations from Trust in Social Networks

  • Jennifer Golbeck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3986)


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.


Social Network Recommender System Trust Rating Trust Relationship Personalized Rating 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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) Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Massa, P., Avesani, P.: Trust-aware Collaborative Filtering for Recommender Systems. In: Proceedings of the International Conference on Cooperative Information Systems (CoopIS) (2004)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Golbeck, J.: Computing and Applying Trust in Web-Based Social Networks, Ph.D. Dissertation, University of Maryland, College Park (2005)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    American Film Institute, 100 Years, 100 Movies,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jennifer Golbeck
    • 1
  1. 1.University of Maryland, College ParkCollege ParkUSA

Personalised recommendations