Building a Social Recommender System by Harvesting Social Relationships and Trust Scores between Users

  • Daniel Mican
  • Loredana Mocean
  • Nicolae Tomai
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 127)


Recommender systems were created to guide the user in a personalized way to interesting resources and to help users cope with the problem of information overload. A system’s ability to adapt to the users’ needs is based on gathering user-generated collective intelligence. In this paper, we present WSNRS, the system proposed for recommending content within social networks. The main goal of the system is to identify and filter the recently published valuable resources while taking into account the interactions and the relationships the user has within social structures. The interactions are logged and aggregated in order to determine the trust scores between users. Using the scores obtained, one can identify the types of relationship established between users; the scores will then be integrated into an adaptive global model used for recommending resources. Our approach presents several advantages over classic CF-based approaches and content-based recommendations regarding cold start, scalability and serendipitous recommendations. We will illustrate this with a case study that we made using data provided by the implementation of the system in a real online social network.


Recommender systems Social networks Trust in social networks Social networks recommender system Collective intelligence 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Groh, G., Ehmig, C.: Recommendations in taste related domains: collaborative filtering vs. social filtering. In: GROUP 2007: Proceedings of the 2007 International ACM Conference on Supporting Group Work, pp. 127–136. ACM, New York (2007)CrossRefGoogle Scholar
  2. 2.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)Google Scholar
  3. 3.
    Hang, C.W., Singh, M.P.: Trust-Based Recommendation Based on Graph Similarity. In: The 13th AAMAS Workshop on Trust in Agent Societies, Trust (2010)Google Scholar
  4. 4.
    Hogg, T.: Inferring preference correlations from social networks. J. Electronic Commerce Research and Applications 9(1), 29–37 (2010)CrossRefGoogle Scholar
  5. 5.
    Kim, H.-N., Ji, A.-T., Jo, G.-S.: Enhanced Prediction Algorithm for Item-Based Collaborative Filtering Recommendation. In: Bauknecht, K., Pröll, B., Werthner, H. (eds.) EC-Web 2006. LNCS, vol. 4082, pp. 41–50. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Konstas, I., Stathopoulos, V., Jose, J.M.: On social networks and collaborative recommendation. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 195–202. ACM, New York (2009)Google Scholar
  7. 7.
    Mican, D., Tomai, N.: Association-Rules-Based Recommender System for Personalization in Adaptive Web-Based Applications. In: Daniel, F., Facca, F.M. (eds.) ICWE 2010. LNCS, vol. 6385, pp. 85–90. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Mican, D., Tomai, N.: Web 2.0 and Collaborative Tagging. In: Proceedings of the 2010 Fifth International Conference on Internet and Web Applications and Services (ICIW 2010), pp. 519–524. IEEE Computer Society, Washington, DC (2010)CrossRefGoogle Scholar
  9. 9.
    Petrusel, R., Stanciu, P.L.: Making Recommendations for Decision Processes Based on Aggregated Decision Data Models. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds.) BIS 2012. LNBIP, vol. 117, pp. 272–283. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Ramezani, M., Bergman, L., Thompson, R., Burke, R., Mobasher, B.: Selecting and Applying Recommendation Technology. In: Proceedings of International Workshop on Recommendation and Collaboration, in Conjunction with IUI 2008, Canaria, Spain (2008)Google Scholar
  11. 11.
    Ramos, V., Fernandes, C., Rosa, A.: Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes. J. of NMNCSE (2005)Google Scholar
  12. 12.
    Sinha, R.R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: DELOS Workshop: Personalisation and Rec. Systems in Digital Libraries (2001)Google Scholar
  13. 13.
    Walter, F.E., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. J. Autonomous Agents and Multi-Ag. Sys. 16(1), 57–74 (2008)CrossRefGoogle Scholar
  14. 14.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences). Cambridge University Press, New York (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniel Mican
    • 1
  • Loredana Mocean
    • 1
  • Nicolae Tomai
    • 1
  1. 1.Dept. of Business Information SystemsBabes-Bolyai UniversityCluj-NapocaRomania

Personalised recommendations