SoNARS: A Social Networks-Based Algorithm for Social Recommender Systems

  • Francesca Carmagnola
  • Fabiana Vernero
  • Pierluigi Grillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)


User modeling systems have been influenced by the overspread of Web 2.0 and social networks. New systems aimed at helping people finding information of interest and including “social functions” like social networks, tagging, commenting, inserting content, arose. Such systems are the so-called “social recommender systems”. The idea at the base of social recommender systems is that the recommendation of content should follow user’s preferences while social network just represents a group of users joined by some kind of voluntary relation and does not reflect any preference. We claim that social network is a very important source of information to profile users. Moving from theories in social psychology which describe influence dynamics among individuals, we state that joining in a network with other people exposes individuals to social dynamics which can influence their attitudes, behaviours and preferences.

We present in this paper SoNARS, a new algorithm for recommending content in social recommender systems. SoNARS targets users as members of social networks, suggesting items that reflect the trend of the network itself, based on its structure and on the influence relationships among users.


Social Network Recommender System Social Comparison Target User Social Facilitation 
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.
    Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Conference on Uncertainty in Artificial Intelligence (1998)Google Scholar
  2. 2.
    Burke, B.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Carmagnola, F., Cena, F., Console, L., Cortassa, O., Gena, C., Goy, A., Torre, I., Toso, A., Vernero, F.: Tag-based user models for social multi-device adaptive guides. In: User Modeling and User-Adapted Interaction (2008)Google Scholar
  4. 4.
    Dourish, P., Chalmers, M.: Running out of Space: Models of Information Navigation. In: Cockton, G., Draper, S.W., Weir, G.R.S. (eds.) People and Computers IX, Proceedings of HCI 1994, Glasgow, Scotland. Cambridge University Press, Cambridge (1994)Google Scholar
  5. 5.
    Farzan, R., Brusilovsky, P.: Annotated: A social navigation and annotation service for web-based educational resources. In: Proceedings of the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, E-Learn 2006, Honolulu, Hawaii, pp. 2794–2802 (2006)Google Scholar
  6. 6.
    Golbeck, J., Mannes, A.: Using trust and provenance for content filtering on the semantic web. In: Proc. of Workshop on Models of Trust for the Web, at 15th International World Wide Web Conference WWW 2006, Edinburgh, UK, May 22-26 (2006)Google Scholar
  7. 7.
    Granovetter, M.S.: The strength of weak ties. The American Journal of Sociology 78, 1360–1380 (1973)CrossRefGoogle Scholar
  8. 8.
    Heath, T., Motta, E.: Ease of interaction plus ease of integration: Combining web2.0 and the semantic web in a reviewing site. Web Semantics 6(1), 76–83 (2008)CrossRefGoogle Scholar
  9. 9.
    Herlocker, J., Konstan, J., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  10. 10.
    Kobsa, A., Koenemann, J., Pohl, W.: Personalized hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review 16(2), 111–155 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Livolsi, M.: L’ Italia che cambia. La Nuova Italia (1993)Google Scholar
  12. 12.
    Mcpherson, M., Lovin, L.S., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27(1), 415–444 (2001)CrossRefGoogle Scholar
  13. 13.
    O’Sullivan, D., Wilson, D., Smyth, B.: Improving case-based recommendation: A collaborative filtering approach. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS, vol. 2416, p. 278. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW 1994, pp. 175–186. Chapel Hill, NC (1994)CrossRefGoogle Scholar
  15. 15.
    Resnick, P., Varian, H.: Recommender systems. Introduction to special section of Communications of the ACM 40(3) (March 1997)Google Scholar
  16. 16.
    Scott, J.P.: Social Network Analysis: A Handbook. SAGE Publications, Thousand Oaks (2000)Google Scholar
  17. 17.
    Turner, J.C.: Social Influence. Brooks/Cole, Pacific Grove (1991)Google Scholar
  18. 18.
    Van Mark, S., Brussee, R., van Vliet, H., Gazendam, L., van Houten, Y., Veenstra, M.: On the importance of “who tagged what”. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 552–561. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    Webster, A., Vassileva, J.: The keepup recommender system. In: Konstan, J.A., Riedl, J., Smyth, B. (eds.) RecSys, pp. 173–176. ACM, New York (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Francesca Carmagnola
    • 1
  • Fabiana Vernero
    • 1
  • Pierluigi Grillo
    • 1
  1. 1.Dipartimento di InformaticaUniversità di TorinoTorinoItaly

Personalised recommendations