Trust Distrust Enhanced Recommendations Using an Effective Similarity Measure

  • Stuti Chug
  • Vibhor Kant
  • Mukesh Jadon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10682)


Collaborative filtering (CF), the most prevalent technique in the area of recommender systems (RSs), provides suggestions to users based on the tastes of their similar users. However, the new user and sparsity problems, degrade its efficiency of recommendations. Trust can enhance the recommendation quality by mimicking social dictum “friend of a friend will be a friend”. However distrust, the another face of coin is yet to be explored along with trust in the area of RSs. Our work in this paper is an attempt toward introducing trust-distrust enhanced recommendations based on the novel similarity measure that combines user ratings and trust values for generating more quality recommendations. Our approach also exploits distrust links among users and analyses their propagation effects. Further, distrust values are also used for filtering more distrust-worthy neighbours from the neighbourhood set. Our experimental results show that our proposed approaches outperform the traditional CF and existing trust enhanced approaches in terms of various performance measures.


Trust and distrust models Recommender systems Trust network Collaborative filtering Cold start and sparsity problem 


  1. 1.
    Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: Meersman, R., Tari, Z. (eds.) OTM 2004. LNCS, vol. 3290, pp. 492–508. Springer, Heidelberg (2004). CrossRefGoogle Scholar
  2. 2.
    Guha, R., et al.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web. ACM (2004)Google Scholar
  3. 3.
    Adamavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  4. 4.
    Golbeck, J.A.: Computing and applying trust in web-based social networks. Dissertation (2005)Google Scholar
  5. 5.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces. ACM (2005)Google Scholar
  6. 6.
    Lathia, N., Hailes, S., Capra, L.: Trust-based collaborative filtering. In: Karabulut, Y., Mitchell, J., Herrmann, P., Jensen, C.D. (eds.) IFIPTM 2008. IFIPTIFIP, vol. 263, pp. 119–134. Springer, Boston (2008). CrossRefGoogle Scholar
  7. 7.
    Bharadwaj, K.K., Al-Shamri, M.Y.H.: Fuzzy computational models for trust and reputation systems. Electron. Commer. Res. Appl. 8(1), 37–47 (2009)CrossRefGoogle Scholar
  8. 8.
    Victor, P., et al.: Gradual trust and distrust in recommender systems. Fuzzy Sets Syst. 160(10), 1367–1382 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Victor, P., et al.: Trust-and distrust-based recommendations for controversial reviews. In: Web Science Conference (WebSci 2009: Society On-Line). No. 161 (2009)Google Scholar
  10. 10.
    Victor, P., et al.: Practical aggregation operators for gradual trust and distrust. Fuzzy Sets Syst. 184(1), 126–147 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Kant, V., Bharadwaj, K.K.: Fuzzy computational models of trust and distrust for enhanced recommendations. Int. J. Intell. Syst. 28(4), 332–365 (2013)CrossRefGoogle Scholar
  12. 12.
    Bobadilla, J., et al.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)CrossRefGoogle Scholar
  13. 13.
    Anand, D., Bharadwaj, K.K.: Pruning trust-distrust network via reliability and risk estimates for quality recommendations. Social Network Anal. Min. 3(1), 65–84 (2013)CrossRefGoogle Scholar
  14. 14.
    Lee, W.-P., Ma, C.-Y.: Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks. Knowl.-Based Syst. 106, 125–134 (2016)CrossRefGoogle Scholar
  15. 15.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  16. 16.
    Victor, P., et al.: A comparative analysis of trust-enhanced recommenders for controversial items. In: ICWSM (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThe LNM Institute of Information TechnologyJaipurIndia

Personalised recommendations