Skip to main content
SpringerLink
Log in

On evaluating the effectiveness of rating similarity-based trust

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Trust metrics based on rating similarity are currently being employed to augment collaborative filtering in recommender systems with a view to mitigate the problem of data sparsity and improve their performance. Surprisingly, seldom little has been done to analyze the correlation between trust and rating similarity. In this paper, we examine the relationship between rating similarity-based implicit trust and trust that is explicitly expressed by users. We develop a logistic regression model to predict the likelihood of trust between users based on rating similarity between them. We use the large, publicly available Epinion dataset to derive the best fitting model parameters and validate the model. An empirical analysis of the relationship between rating similarity and explicit trust reveals a positive albeit very weak correlation between them. Further, we conduct experiments on implicit similarity trust prediction for varying values of similarity threshold and co-rated item count threshold. In general, results indicate low values of precision, recall, and coverage over the entire range of similarity threshold. The best precision that was obtained was 23.8 %, but with a coverage of only 0.6 % users. Best coverage value was 19.7 % but with precision of 3.2 % only. In fact, better precision can be obtained by predicting trust only on the basis of number of co-rated items without considering rating similarity between users. We therefore conclude that implicit trust based on rating similarity is not a reliable representation of trust between users.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. www.trustlet.org/wiki/Epinions_datasets.

References

  • Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

    Article  Google Scholar 

  • Dwyer C, Hiltz SR, Passerini K (2007) Trust and privacy concern within social networking sites: a comparison of facebook and myspace. In Proceedings of the 13th Americas conference on information systems

  • Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. SIGCOMM 1999:251–262

    Article  MATH  Google Scholar 

  • Golbeck J (2009) Trust and nuanced profile similarity in online social networks. ACM Trans Web (TWEB). doi:10.1145/1594173.1594174

    Google Scholar 

  • Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagation of trust and distrust. WWW 2004:403–412. doi:10.1145/988672.988727

    Google Scholar 

  • Guo G, Zhang J, Thalmann D, Basu A, Yorke-Smith N (2014) From ratings to trust: an empirical study of implicit trust in recommender systems. In: Symposium on applied computing—recommender systems 2014

  • Huberman BA, Romero DM, Wu F (2009) Social networks that matter: twitter under the microscope. First Monday, 14(1). doi:10.5210/fm.v14i1.2317

  • Hwang CS, Chen YP (2007) Using trust in collaborative Filtering recommendation. In: Hiroshi G. Okuno, Moonis Ali (ed) New trends in applied artificial intelligence Hiroshi G. Okuno, Moonis Ali (eds) 2007. Springer, Berlin, pp 1052–1060. doi:10.1007/978-3-540-73325-6_105

  • Lathia N, Hailes S, Capra L (2008) Trust-based collaborative filtering. In: Karabulut Y, Mitchell J, Herrmann P, Jensen CD (eds) IFIP international federation of information processing, vol 263. Trust management II, Springer, US, pp 119–134. doi:10.1007/978-0-387-09428-1_8

  • Lee DH, Brusilovsky P (2009) Does trust influence information similarity? In: Song R, Korba L, Yee E (eds) Proceedings of the 2009 ACM conference on recommender systems, pp 71–74

  • Massa P (2007) A survey of trust use and modelling in real online systems. In: Song R, Korba L, Yee G (eds) Trust in e-services: technologies, practices and challenges. IGI Publishing, Hershey, pp 51–83. doi:10.4018/978-1-59904-207-7.ch003

  • O’Donovan J, Smyth B (2005) Trust in Recommender Systems. In Proceedings of 10th international conference on intelligent user interfaces, 2005, pp 167–174

  • Papagelis M, Plexousakis D, Kutsuras T (2005) Alleviating the sparsity problem of collaborative filtering using trust inferences. In: Herrmann P, Issarny V, Shiu S (eds) iTrust 2005, Springer, Berlin, pp 224–239. doi:10.1007/11429760_16

  • Pitsilis G, Marshall LF (2004) A model of trust derivation from evidence for use in recommendation systems. In: Technical report. University of Newcastle upon Tyne, 2004

  • Podobnik V, Striga D, Jandras A, Lovrek I (2012) How to Calculate Trust between Social Network Users? In: Proceedings of the 20th international conference on software, telecommunication and computer networks, 2012

  • Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of net news. In: Proceedings of ACM conference on computer-supported cooperative work, sharing information and creating meaning, 1994, pp 175–186

  • Seltman HJ (2012) Experimental design and analysis. Carnegie Mellon University Press. http://www.stat.cmu.edu/~hseltman/309/Book/Book.pdf. Accessed 2 May 2016

  • Sherchan W, Nepal S, Paris C (2013) A survey of trust in social networks. ACM Comput Surv 45(4):47

    Article  Google Scholar 

  • Singh S, Bawa S (2007) Privacy, trust and policy based authorization framework for services in distributed environments. Int J Comput Sci 2(2):85–92

    Google Scholar 

  • Ugander J, Karrer B, Backstrom L, Marlow C (2011) The anatomy of the facebook social graph. CoRR arXiv:1111.4503

  • Young AL, Quan-Haase A (2009) Information revelation and internet privacy concerns on social network sites: a case study of facebook. In: Proceedings of the international conference on communities and technologies, 2009, pp 265–274

  • Yuan W, Shu L, Chao HC, Guan D, Lee YK, Lee S (2010) ITARS: trust-aware recommender system using implicit trust networks. IET Commun 4(14):1709–1721

    Article  Google Scholar 

  • Zafarani R, Abbasi M A, Liu H (2014) Social media mining: an introduction. Cambridge University Press, ISBN 9781107018853

  • Zhou W, Duan W, Piramuthu S (2014) A social network matrix for implicit and explicit social network plates. Decis Support Syst 68:89–97

    Article  Google Scholar 

  • Ziegler CN, Golbeck J (2007) Investigating interactions of trust and interest similarity. Decis Support Syst 43(2):460–475

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Netaji Subhas Institute of Technology, New Delhi, India

    Shampa Chakraverty, Anuradha Yadav & Ritu Sibal

Authors
  1. Shampa Chakraverty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anuradha Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ritu Sibal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anuradha Yadav.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chakraverty, S., Yadav, A. & Sibal, R. On evaluating the effectiveness of rating similarity-based trust. Soc. Netw. Anal. Min. 6, 98 (2016). https://doi.org/10.1007/s13278-016-0395-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-016-0395-0

Keywords

Search

Navigation