Skip to main content
SpringerLink
Log in

Collaborative web search: a robustness analysis

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Collaborative web search utilises past search histories in a community of like-minded users to improve the quality of search results. Search results that have been selected by community members for past queries are promoted in response to similar queries that occur in the future. The I-SPY system is one example of such a collaborative approach to search. As is the case with all open systems, however, it is difficult to establish the integrity of those who access a system and thus the potential for malicious attack exists. In this paper we investigate the robustness of the I-SPY system to attack. In particular, we consider attack scenarios whereby malicious agents seek to promote particular result pages within a community. In addition, we analyse robustness in the context of community homogeneity, and we show that this key characteristic of communities has implications for system robustness.

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

Similar content being viewed by others

References

  • Bhaumik R, Burke R, Mobasher B (2007) Effectiveness of crawling attacks against web-based recommender systems. In: Proceedings of the 5th workshop on intelligent techniques for web personalization (ITWP-07)

  • Burke R (2002) Hybrid recommender systems: Survey and experiments. User Model User–Adapt Interact 12(4): 331–370

    Article  MATH  Google Scholar 

  • Chirita PA, Nejdl W, Zamfir C (2005) Preventing shilling attacks in online recommender systems. In Proceedings of the ACM workshop on web information and data management (WIDM’2005) pp 67–74, Germany

  • Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content and collaborative filters in an on-line newspaper. In: Proceedings of the ACM SIGIR workshop on recommender systems: algorithms and evaluation, 22nd international conference on research and development in information retrieval pp 15–22

  • Coyle M, Smyth B (2007) Supporting intelligent web search. ACM Trans Internet Technol 7(4) (Article No. 20)

  • Herlocker J, Konstan J, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd international ACM SIGIR conference on research and development in information retrieval pp 230–237, Berkeley

  • Koutrika G, Effendi FA, Gyongyi Z, Haymann P, Garcia-Molina H (2007) Combating spam in tagging systems. In: Proceedings of 3rd international workshop on adversarial information retrieval on the web (AIRWeb’07)

  • Lam SK, Riedl J (2004) Shilling recommender systems for fun and profit. In: Proceedings of the 13th international world wide web conference pp 393–402, New York

  • Massa P, Avesani P (2007) Trust-aware recommender systems. In: Proceedings of the ACM conference on recommender systems (RecSys’07) pp 17–24, Cyprus

  • Mehta B, Hofmann T, Frankhauser P (2007) Lies and propaganda: Detecting spam users in collaborative filtering. In: Proceedings of the 12th international conference on intelligent user interfaces (IUI-07) pp 14–21

  • Mobasher B, Burke R, Bhaumik R, Williams C (2005) Effective attack models for shilling item-based collaborative filtering system. In: Proceedings of the 2005 WebKDD workshop (KDD’2005), Chicago

  • Mobasher B, Burke R, Bhaumik R, Williams C (2007) Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans Internet Technol 7(4) (Article 23)

  • O’Donovan J, Smyth B (2006) Is trust robust? An analysis of trust-based recommendation. In: Proceedings of the 11th international conference on intelligent user interfaces (IUI’06) pp 101–108, Sydney

  • O’Mahony MP, Hurley NJ, Kushmerick N, Silvestre GCM (2004) Collaborative recommendation: a robustness analysis. ACM Trans Internet Technol (TOIT), Special Issue on Machine Learning for the Internet 4(4): 344–377

    Google Scholar 

  • O’Mahony MP, Hurley NJ, Silvestre GCM (2005) Recommender systems: attack types and strategies. In: Proceedings of the 20th national conference on artificial intelligence (AAAI-05) pp 334–339

  • O’Mahony MP, Hurley NJ, Silvestre GCM (2006) Detecting noise in recommender system databases. In: Proceedings of the international conference on intelligent user interfaces (IUI’06) pp 109–115

  • Resnick P, Varian HR (1997) Recommender systems—introduction to the special section. Commun ACM 40(3): 56–58

    Article  Google Scholar 

  • Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: An open architecture for collaborative filtering of netnews. In: Proceedings of the ACM conference on computer supported cooperative work (CSCW’94) pp 175–186

  • Sandvig J, Mobasher B, Burke R (2007) Robustness of collaborative recommendation based on association rule mining. In: Proceedings of the ACM conference on recommender systems (RecSys’07) pp 105–111, Minneapolis

  • Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the tenth international world wide web conference pp 285–295

  • Shardanand U, Maes P (1995) Social information filtering: Algorithms for automating word of mouth. In: Proceedings of ACM conference on human factors in computing systems (CHI’95) pp 210–217, Denver

  • Smyth B, Balfe E (2006) Anonymous personalisation in collaborative web search. J Inform Retr 9(2): 165–190

    Article  Google Scholar 

  • 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) pp 1417–1419, Mexico

  • Smyth B, Balfe E, Freyne J, Briggs P, Coyle M, Boydell O (2004) Exploiting query repetition & regularity in an adaptive community-based web search engine. User Model User-Adapt Interact: J Per Res 14: 383–423

    Article  Google Scholar 

  • Smyth B, Balfe E, Boydell O, Bradley K, Briggs P, Coyle M, Freyne J (2005) A live-user evaluation of collaborative web search. In: Proceedings of the 19th international joint conference on artificial intelligence (IJCAI-05) pp 1419–1424

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Informatics, University College Dublin, Dublin, Ireland

    Michael P. O’Mahony & Barry Smyth

Authors
  1. Michael P. O’Mahony
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barry Smyth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael P. O’Mahony.

Rights and permissions

Reprints and permissions

About this article

Cite this article

O’Mahony, M.P., Smyth, B. Collaborative web search: a robustness analysis. Artif Intell Rev 28, 69–86 (2007). https://doi.org/10.1007/s10462-008-9075-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-008-9075-4

Keywords

Search

Navigation