Skip to main content
SpringerLink
Log in

Improving the accuracy of Business-to-Business (B2B) reputation systems through rater expertise prediction

  • Published:
Computing Aims and scope Submit manuscript

Abstract

Digital ecosystems rely on reputation systems in order to build trust and to foster collaborations among users. Reputation systems are commonplace in the Customer-to-Customer and Business-to-Customer contexts, however, they have not yet found mainstream acceptance in Business-to-Business (B2B) environments. Our first contribution in this paper is to identify the particularities of feedback collection in B2B reputation systems. An issue that we identify is that the reputation target in the B2B context is a business, which requires evaluation on a large number of criteria. We observe that due to the wide variation in user expertise, feedback forms that require users to evaluate all criteria have significant negative consequences for rating accuracy. Our second contribution is to propose an expertise prediction algorithm for B2B reputation systems, which filters the criteria describing the target business such that each user rates only on those criteria that he has expertise in. Experiments based on our real dataset show that the algorithm accurately predicts the expertise of users in given criteria. The algorithm may also increase the motivation of users to submit feedback as well as the confidence of users in B2B reputation systems.

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

References

  1. Adomavicius G, Tuzhilin A (2005) Toward 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. doi:10.1109/TKDE.2005.99

    Google Scholar 

  2. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User Adapt Interact. 12(4):331–370. doi:10.1023/A:1021240730564

  3. Carlsson C (2008) Ratings and reviews for the business user—a presentation of results from the author’s mba dissertation at henley management college, UK. https://sites.google.com/site/businessuserreviews/

  4. Chen M, Singh JP (2001) Computing and using reputations for internet ratings. In: Proceedings of the 3rd ACM conference on electronic commerce, ACM, New York, NY, USA, EC ’01, pp 154–162. doi:10.1145/501158.501175

  5. Cheng J, Condry M, Karduck AP (2013) Call for papers—7th IEEE international conference on digital ecosystems and technologies (IEEE dest 2013). http://dest2013.digital-ecology.org/

  6. Dellarocas C (2003) The digitization of word of mouth: promise and challenges of online feedback mechanisms. Manage Sci 49(10):1407–1424. doi:10.1287/mnsc.49.10.1407.17308

    Google Scholar 

  7. Dellarocas CN, Fan M, Wood CA (2004) Self-interest, reciprocity and participation in online reputation systems. Working Papers 4500–04, MIT Sloan. http://ssrn.com/abstract=585402

  8. Fung H (2011) Rate this page? is coming to the english wikipedia. http://blog.wikimedia.org/2011/07/15/

  9. Golbeck J, Hendler J (2006) Inferring binary trust relationships in web-based social networks. ACM Trans Internet Technol 6(4):497–529. doi:10.1145/1183463.1183470

    Google Scholar 

  10. Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: a constant time collaborative filtering algorithm. Inf Retr 4(2):133–151. doi:10.1023/A:1011419012209

    Google Scholar 

  11. Grcar M (2004) User profiling: collaborative filtering. In: Proceedings of the 7th international multiconference information society IS, pp 75–78

  12. Harper FM, Li X, Chen Y, Konstan JA (2005) An economic model of user rating in an online recommender system. In: Proceedings of the 10th international conference on user modeling. Springer, Berlin, Heidelberg, UM’05, pp 307–316. doi:10.1007/11527886_40

  13. Hasan O, Brunie L, Bertino E (2012) Preserving privacy of feedback providers in decentralized reputation systems. Comput Secur 31(7): 816–826

    Google Scholar 

  14. Hoffman K, Zage D, Nita-Rotaru C (2009) A survey of attack and defense techniques for reputation systems. ACM Comput Surv 41(4)

  15. Houser D, Wooders J (2001) Reputation in auctions: theory and evidence from eBay.Mimeo

  16. Jamieson S (2004) Likert scales: how to (ab)use them. Med Educ 38(12):1217–1218. doi:10.1111/j.1365-2929.2004.02012.x

    Google Scholar 

  17. Jøsang A, Ismail R, Boyd C (2007) A survey of trust and reputation systems for online service provision. Decis Support Syst 43(2):618–644

    Article  Google Scholar 

  18. JStueland V (2004) Supplier evaluation: best practices and creating or improving your own evaluation. In: 89th annual international supplier management conference

  19. Kompass (2012) Le processus d’achat b2b. http://fr.kompass.com/espace-business/index.php?option=com&view

  20. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37. doi:10.1109/MC.2009.263

    Google Scholar 

  21. Lakiotaki K, Matsatsinis NF, Tsoukias A (2011) Multicriteria user modeling in recommender systems. IEEE Intell Syst 26:64–76. doi:10.1109/MIS.2011.33

    Google Scholar 

  22. Lampe C, Garrett RK (2007) It’s all news to me: the effect of instruments on ratings provision. In: Proceedings of the 40th annual Hawaii international conference on system sciences, IEEE Computer Society, Washington, DC, USA, HICSS ’07, pp 180b. doi:10.1109/HICSS.2007.308

  23. Lee S, Song Si, Kahng M, Lee D, Lee Sg (2011) Random walk based entity ranking on graph for multidimensional recommendation. In: Proceedings of the fifth ACM conference on recommender systems, ACM, New York, NY, USA, RecSys ’11, pp 93–100. doi:10.1145/2043932.2043952

  24. Liu L, Munro M (2012) Systematic analysis of centralized online reputation systems. Decis Support Syst 52(2):438–449. doi:10.1016/j.dss.2011.10.003

  25. Liu L, Mehandjiev N, Xu DL (2011) Multi-criteria service recommendation based on user criteria preferences. In: Proceedings of the fifth ACM conference on recommender systems, ACM, New York, NY, USA, RecSys ’11, pp 77–84. doi:10.1145/2043932.2043950

  26. McLaughlin MR, Herlocker JL (2004) A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval, ACM, New York, NY, USA, SIGIR ’04, pp 329–336. doi:10.1145/1008992.1009050

  27. Miller B, Riedl JT, Konstan JA (1997) Experiences with grouplens: making usenet useful again. In: Proceedings of the 1997 Usenix Winter technical conference, pp 219–231

  28. Ortega F, SáNchez JL, Bobadilla J, GutiéRrez A (2013) Improving collaborative filtering-based recommender systems results using pareto dominance. Inf Sci 239:50–61. doi:10.1016/j.ins.2013.03.011

    Google Scholar 

  29. Ozakca M, Lim YK (2006) A study of reviews and ratings on the internet. In: CHI ’06 extended abstracts on human factors in computing systems, ACM, New York, NY, USA, CHI EA ’06, pp 1181–1186. doi:10.1145/1125451.1125673

  30. Pal A, Konstan JA (2010) Expert identification in community question answering: exploring question selection bias. In: Proceedings of the 19th ACM international conference on information and knowledge management, ACM, New York, NY, USA, CIKM ’10, pp 1505–1508. doi:10.1145/1871437.1871658

  31. Parra D, Karatzoglou A, Amatriain X, Yavuz I (2011) Implicit feedback recommendation via implicit-to-explicit ordinal logistic regression mapping. In: In CARS Workshop at RecSys

  32. Pinyol I, Sabater-Mir J (2011) Computational trust and reputation models for open multi-agent systems: a review. Artif Intell Rev. doi:10.1007/s10462-011-9277-z

  33. Resnick P, Kuwabara K, Zeckhauser R, Friedman E (2000) Reputation systems. Commun ACM 43(12):45–48

    Article  Google Scholar 

  34. Sabater J, Sierra C (2005) Review on computational trust and reputation models. Artif Intell Rev 24(1):33–60

    Article  MATH  Google Scholar 

  35. Schafer JB, Konstan J, Riedi J (1999) Recommender systems in e-commerce. In: Proceedings of the 1st ACM conference on electronic commerce, ACM, New York, NY, USA, EC ’99, pp 158–166

  36. Sparling EI, Sen S (2011) Rating: how difficult is it? In: Proceedings of the fifth ACM conference on recommender systems, ACM, New York, NY, USA, RecSys ’11, pp 149–156

  37. Swearingen K, Sinha R (2002) Interaction design for recommender systems. In: Designing Interactive Systems, Vol. 6, No. 12. ACM Press, pp. 312–334

  38. Yesilada Y, Brajnik G, Harper S (2009) How much does expertise matter? A barrier walkthrough study with experts and non-experts. In: Proceedings of the 11th international ACM SIGACCESS conference on computers and accessibility, ACM, New York, NY, USA, Assets ’09, pp 203–210

  39. Zacharia G (2000) Trust management through reputation mechanisms. Appl Artif Intell 14:881–907

    Article  Google Scholar 

  40. Zhang J, Ackerman MS, Adamic L, Nam KK (2007) Qume: a mechanism to support expertise finding in online help-seeking communities. In: Proceedings of the 20th annual ACM symposium on user interface software and technology, ACM, New York, NY, USA, UIST ’07, pp 111–114

Download references

Author information

Authors and Affiliations

  1. Albert-Ludwigs-Universität Freiburg, Friedrichstr. 39, 79098 , Freiburg, Germany

    Heidi Dikow

  2. University of Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205, 69621 , Lyon-Villeurbanne, France

    Omar Hasan & Lionel Brunie

  3. University of Passau, Innstrasse 43, 94032 , Passau, Germany

    Harald Kosch

  4. ALG-Attestation Légale, 20 Bd Eugène Deruelle-CS 63753, 69432 , Lyon, France

    Renaud Sornin

Authors
  1. Heidi Dikow
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omar Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Harald Kosch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lionel Brunie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Renaud Sornin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omar Hasan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dikow, H., Hasan, O., Kosch, H. et al. Improving the accuracy of Business-to-Business (B2B) reputation systems through rater expertise prediction. Computing 97, 29–49 (2015). https://doi.org/10.1007/s00607-013-0345-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-013-0345-x

Keywords

Mathematics Subject Classification

Search

Navigation