Manipulating the relevance models of existing search engines

Authors:
Oisín Boydell;Cathal Gurrin;Alan F. Smeaton;Barry Smyth
Affiliations:
Adaptive Information Cluster, Smart Media Institute, Department of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland;Adaptive Information Cluster, Center for Digital Video Processing, Dublin City University, Glasnevin, Dublin 9, Ireland;Adaptive Information Cluster, Center for Digital Video Processing, Dublin City University, Glasnevin, Dublin 9, Ireland;Adaptive Information Cluster, Smart Media Institute, Department of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland
Venue:
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Year:
2005

Citing 1
Cited 2

Further Experiments on Collaborative Ranking in Community-Based Web Search

Artificial Intelligence Review

Evaluating the impact of selection noise in community-based web search

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating the impact of selection noise in community-based web search

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

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Abstract

Collaborative search refers to how the search behavior of communities of users can be used to influence the ranking of search results. In this poster we describe how this technique, as instantiated in the I-SPY meta-search engine can be used as a general mechanism for implementing a different relevance feedback strategy. We evaluate a relevance feedback strategy based on anchor-text and query similarity using the TREC2004 Terabyte track document collection.