Skip to main content
Log in

On the role of trust in collaborative Web search

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Recommender systems combine ideas from information retrieval, user modelling, and artificial intelligence to focus on the provision of more intelligent and proactive information services. As such, recommender systems play an important role when it comes to assisting the user during both routine and specialised information retrieval tasks. Like any good assistant it is important that users can trust in the ability of a recommender system to respond with timely and relevant suggestions. In this paper, we will look at a collaborative recommendation system operating in the domain of Web search. We will show how explicit models of trust can help to inform more reliable recommendations that translate into more relevant search results. Moreover, we demonstrate how the availability of this trust-model facilitates important interface enhancements that provide a means to declare the provenance of result recommendations in a way that will allow searchers to evaluate their likely relevance based on the reputation and trustworthiness of the recommendation partners behind these suggestions.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Balfe E, Smyth B (2004) Case-based collaborative Web search. In: Proceedings of the 7th European conference on cased based reasoning. pp 489–503

  2. Bilgic M, Mooney RJ (2005) Explaining recommendations: satisfaction vs. promotion. In: Proceedings of beyond personalization 2005: a workshop on the next stage of recommender systems research at the 2005 international conference on intelligent user interfaces. San Diego, CA. http://www.cs.umd.edu/mbilgic/publications/explainingrecs.pdf

  3. Boydell O, Smyth B, Gurrin C, Smeaton AF (2005) A study of selection noise in collaborative Web search. In: Proceedings of the 19th international joint conference on artificial intelligence, IJCAI-05. Edinburgh, Scotland, pp 1595–1597

  4. Carter J and Ghorbani AA (2004). Towards a formalization of value-centric trust in agent societies. Int J Web Intelligence and Agent Systems 2(3): 167–184

    Google Scholar 

  5. Doyle D, Cunningham P, Bridge DG, Rahman Y (2004) Explanation oriented retrieval. In: Funk P, González-Calero PA (eds) ECCBR: Lecture notes in computer science, vol 3155 Springer, pp 157–168

  6. Froehlich TJ (1994). Relevance reconsidered—towards an agenda for the 21st century: introduction to special topic issue on relevance research. J Am Soc Inf Sci 45(3): 124–134

    Article  Google Scholar 

  7. Guha R, Kumar R, Raghaven P, Tomkins A (2004) Propagation of trust and distrust. In: Proceedings of WWW 04. ACM, pp 403–412

  8. Gyöngyi Z, Garcia-Molina H, Pedersen J (2004) Combating Web spam with trustrank. In: Proceedings of the 30th international conference on very large databases. Morgan Kaufmann, pp 576–587

  9. Joachims T, Freitag D, Mitchell TM (1997) Web watcher: a tour guide for the World Wide Web. In: IJCAI (1). pp 770–777

  10. Mayer RC, Davis JH and Schoorman F (1995). An integrative model of organizational trust. Acad Manage Rev 20(3): 709–734

    Article  Google Scholar 

  11. Massa P, Avesani P (2004) Trust-aware collaborative filtering for recommender systems. In: CoopIS/DOA/ODBASE (1). pp 492–508

  12. McCarthy K, Reilly J, McGinty L, Smyth B (2004) On the dynamic generation of compound critiques in conversational recommender systems. In: Bra PD (ed) Proceedings of the third international conference on adaptive hypermedia and web-based systems (AH-04). Springer. Eindhoven, The Netherlands, pp 176–184

  13. McSherry D (2004) Incremental relaxation of unsuccessful queries. In: Calero PAG, Funk P (eds) Proceedings of the european conference on case-based reasoning (ECCBR-04). Springer. Madrid, Spain, pp 331–345

    Google Scholar 

  14. Metaxas PT, DeStefano J (2005) Web spam, propaganda and trust. In: First international workshop on adversarial information retrieval on the Web. http://airweb.cse.lehigh.edu/2005/metaxas.pdf

  15. O’Donovan J, Smyth B (2005a) Eliciting trust values from recommendation errors. In: Russell I, Markov Z (eds) FLAIRS Conference. AAAI Press, pp 289–294

  16. O’Donovan J, Smyth B (2005b) Trust in recommender systems. In: IUI ’05: Proceedings of the 10th international conference on intelligent user interfaces. ACM Press, New York, NY, USA, pp 167–174

  17. O’Mahony MP, Hurley NJ, Kushmerick N and Silvestre GCM (2004). Collaborative recommendation: a robustness analysis. ACM Transactions on Internet Technol—Special issue mach learn internet 4(4): 344–377

    Google Scholar 

  18. Reilly J, McCarthy K, McGinty L, Smyth B (2004) Dynamic critiquing. In: Calero PAG, Funk P (eds) Proceedings of the European conference on case-based reasoning (ECCBR-04). Springer. Madrid, Spain, pp 763–777

    Google Scholar 

  19. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 conference on computer supported collaborative work. pp 175–186

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

    Article  Google Scholar 

  21. Resnick P, Zeckhauser R (2001) Trust among strangers in internet transactions: empirical analysis of eBay’s reputation system. Technical report, University of Michigan. http://www.si.umich.edu/presnick/papers/ebayNBER/

  22. Richardson M, Agrawal R, Domingos P (2003) Trust management for the semantic Web. In: International semantic Web conference. pp 351–368

  23. Schafer JB, Konstan JA and Riedl J (2001). E-commerce recommendation applications. Data Mining Knowledge Discovery 5(1/2): 115–153

    Article  MATH  Google Scholar 

  24. Smyth B, Balfe E, Briggs P, Coyle M, Freyne J (2003) Collaborative Web search. In: Proceedings of the 18th international joint conference on artificial intelligence, IJCAI-03. Morgan Kaufmann. Acapulco, Mexico, pp 1417–1419

  25. Smyth B, Balfe E, Freyne J, Briggs P, Coyle M and Boydell O (2004a). Exploiting query repetition and regularity in an adaptive community-based Web search engine. User Model User-Adapt Interact: J Personal Res 14(5): 383–423

    Article  Google Scholar 

  26. Smyth B, McGinty L, Reilly J, McCarthy K (2004b) Compound critiques feedback for conversational recommender systems. In: Zhong N, Tirri H, Yao Y, Zhou L, Liu J, Cercone N (eds) IEEE/WIC/ACM International conference on Web intelligence (WI’04). IEEE Press. Beijing, China, pp. 145–151

  27. Yahoo!: Accessed: 11/11/2005, My Web 2.0. http://myweb2.search.yahoo.com

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter Briggs.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Briggs, P., Smyth, B. On the role of trust in collaborative Web search. Artif Intell Rev 25, 97–117 (2006). https://doi.org/10.1007/s10462-007-9025-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-007-9025-6

Keywords

Navigation