Abstract
The so-called Social Web has helped to change the very nature of the Internet by emphasising the role of our online experiences as new forms of content and service knowledge. In this paper we describe an approach to improving mainstream Web search by harnessing the search experiences of groups of like-minded searchers.We focus on the HeyStaks system (www.heystaks.com) and look in particular at the experiential knowledge that drives its search recommendations. Specifically we describe how this knowledge can be noisy, and we describe and evaluate a recommendation technique for coping with this noise and discuss how it may be incorporated into HeyStaks as a useful feature.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
P. Briggs and B. Smyth. Provenance, trust, and sharing in peer-to-peer case-based web search. In ECCBR, pages 89–103, 2008.
R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331–370, 2002.
P. A. Chirita, W. Nejdl, R. Paiu, and C. Kohlschütter. Using odp metadata to personalize search. In SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 178–185, New York, NY, USA, 2005. ACM.
A. Cordier, B. Mascret, and A. Mille. Extending Case-Based reasoning with traces. In Grand Challenges for reasoning from experiences, Workshop at IJCAI’09, July 2009.
I. Koychev and I. Schwab. Adaptation to drifting user’s interests. In Proceedings of ECML2000 Workshop: Machine Learning in New Information Age, pages 39–46, 2000.
S. K. Lam and J. Riedl. Shilling recommender systems for fun and profit. In Proceedings of the 13th international conference on World Wide Web, pages 393–402, New York, NY, USA, 2004. ACM.
S. Ma, X. Li, Y. Ding, M. E. Orlowska, B. Benatallah, F. Casati, D. Georgakopoulos, C. Bartolini, W. Sadiq, and C. Godart. A recommender system with Interest-Drifting. LECTURE NOTES IN COMPUTER SCIENCE, 4831:633, 2007.
M. R. Morris. A survey of collaborative web search practices. In CHI, pages 1657–1660, 2008.
M. R. Morris and E. Horvitz. SearchTogether: an interface for collaborative web search. In Proceedings of the 20th annual ACM symposium on User interface software and technology, pages 3–12, Newport, Rhode Island, USA, 2007. ACM.
J. O’Donovan and B. Smyth. Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces, pages 167–174, San Diego, California, USA, 2005. ACM.
M. P. O’Mahony, N. J. Hurley, and G. C. Silvestre. Detecting noise in recommender system databases. In Proceedings of the 11th international conference on Intelligent user interfaces, pages 109–115, Sydney, Australia, 2006. ACM.
J. Pujol, R. Sanguesa, and J. Bermudez. Porqpine: A distributed and collaborative search engine. In Proc. 12th Intl. World Wide Web Conference, 2003.
J. R. Quinlan. C4. 5: programs for machine learning. Morgan Kaufmann, 1993.
M. C. Reddy and P. R. Spence. Collaborative information seeking: A field study of a multidisciplinary patient care team. Inf. Process. Manage., 44(1):242–255, 2008.
J. B. Schafer, J. A. Konstan, and J. Riedl. Meta-recommendation systems: user-controlled integration of diverse recommendations. In Proceedings of the eleventh international conference on Information and knowledge management, pages 43–51. ACM New York, NY, USA, 2002.
B. Smyth. A community-based approach to personalizing web search. IEEE Computer, 40(8):42–50, 2007.
B. Smyth, P. Briggs, M. Coyle, and M. O’Mahony. Google? shared! a case-study in social web search. In Proceedings of the 1st and 17th International Conference on User Modeling, Adaptation and Personalization (UMAP ’09), Trento, Italy, 2009. Springer.
B. Smyth and P. Champin. The experience web: A Case-Based reasoning perspective. In Grand Challenges for reasoning from experiences, Workshop at IJCAI’09, July 2009.
B. Smyth and M. T. Keane. Remembering to forget: A Competence-Preserving case deletion policy for Case-Based reasoning systems. In IJCAI, pages 377–383, 1995. Best paper award.
B. Smyth and E. McKenna. Competence models and the maintenance problem. Computational Intelligence, 17(2):235–249, 2001.
J.-T. Sun, H.-J. Zeng, H. Liu, Y. Lu, and Z. Chen. Cubesvd: a novel approach to personalized web search. In WWW ’05: Proceedings of the 14th international conference on World Wide Web, pages 382–390, New York, NY, USA, 2005. ACM Press.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag London
About this paper
Cite this paper
Champin, PA., Briggs, P., Coyle, M., Smyth, B. (2010). Coping with Noisy Search Experiences. In: Bramer, M., Ellis, R., Petridis, M. (eds) Research and Development in Intelligent Systems XXVI. Springer, London. https://doi.org/10.1007/978-1-84882-983-1_1
Download citation
DOI: https://doi.org/10.1007/978-1-84882-983-1_1
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-84882-982-4
Online ISBN: 978-1-84882-983-1
eBook Packages: Computer ScienceComputer Science (R0)