Advertisement

Leveraging Trust Behaviour of Users for Group Recommender Systems in Social Networks

  • Nirmal Choudhary
  • K. K. Bharadwaj
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 771)

Abstract

Group recommender systems (GRSs) provide recommendations to groups, i.e., they take all individual group member preferences into account and satisfy them optimally with a sequence of items. Few researchers have considered the behaviour of the users for group recommendation. In our work, we have exploited the trust factor among users to make group recommendations so as to satisfy the preferences of all the users. We present a novel approach to GRSs that takes into account the similarity- and knowledge-based trust between group members to enhance the quality of GRSs. The effectiveness of trust-based GRSs is compared with a baseline technique, the least-misery strategy, and it is observed that the results of computational experiments establish the superiority of our proposed model over the baseline GRSs technique.

Keywords

Group recommender systems Similarity Knowledge Trust 

References

  1. 1.
    Adomavicius, G., and A. Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17 (6): 734–749.CrossRefGoogle Scholar
  2. 2.
    Baltrunas, L., T. Makcinskas, and F. Ricci. 2010. Group recommendations with rank aggregation and collaborative filtering. In Proceedings of the fourth ACM conference on recommender systems, 119–126.Google Scholar
  3. 3.
    Bharadwaj, K.K., and M.Y.H. Al-Shamri. 2009. Fuzzy computational models for trust and reputation systems. Electronic Commerce Research and Applications 8 (1): 37–47.CrossRefGoogle Scholar
  4. 4.
    Jameson, A., and B. Smyth. 2007. Recommendation to groups. In The adaptive web, 596–627.Google Scholar
  5. 5.
    Kant, V., and K.K. Bharadwaj. 2013. Fuzzy computational models of trust and distrust for enhanced recommendations. International Journal of Intelligent Systems 28 (4): 332–365.CrossRefGoogle Scholar
  6. 6.
    Kong, D.T., R.B. Lount Jr., M. Olekalns, and D.L. Ferrin. 2017. Advancing the scientific understanding of trust in the contexts of negotiations and repeated bargaining. Journal of Trust Research 7 (1): 15–21.CrossRefGoogle Scholar
  7. 7.
    Masthoff, J. 2004. Group modeling: Selecting a sequence of television items to suit a group of viewers. In Personalized digital television, 93–141.CrossRefGoogle Scholar
  8. 8.
    McCarthy, J.F. 2002. Pocket restaurant finder: A situated recommender system for groups. In Workshop on mobile Ad-Hoc communication at ACM conference on human factors in computer systems.Google Scholar
  9. 9.
    McCarthy, J.F., and T.D. Anagnost. 1998. MusicFX: An arbiter of group preferences for computer supported collaborative workouts. In Proceedings of ACM conference on computer supported cooperative work, 363–372.Google Scholar
  10. 10.
    McCarthy, K., L. McGinty, B. Smyth, and M. Salamó. 2006. The needs of the many: A case-based group recommender system. In ECCBR, 4106, 196–210.Google Scholar
  11. 11.
    O’connor, M., D. Cosley, J.A. Konstan, and J. Riedl. 2001. PolyLens: A recommender system for groups of users. In ECSCW, 199–218.Google Scholar
  12. 12.
    Yu, Z., X. Zhou, Y. Hao, and J. Gu. 2006. TV program recommendation for multiple viewers based on user profile merging. User Modeling and User-Adapted Interaction 16 (1): 63–82.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

Personalised recommendations