Selectively diversifying Web search results

Santos, R.L.T., Macdonald, C. and Ounis, I. (2010) Selectively diversifying Web search results. In: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM'10), Toronto, ON, Canada, 26-30 October 2010, pp. 1179-1188. (doi:10.1145/1871437.1871586)

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Publisher's URL: http://portal.acm.org/citation.cfm?doid=1871437.1871586

Abstract

Search result diversification is a natural approach for tackling ambiguous queries. Nevertheless, not all queries are equally ambiguous, and hence different queries could benefit from different diversification strategies. A more lenient or more aggressive diversification strategy is typically encoded by existing approaches as a trade-off between promoting relevance or diversity in the search results. In this paper, we propose to learn such a trade-off on a per-query basis. In particular, we examine how the need for diversification can be learnt for each query - given a diversification approach and an unseen query, we predict an effective trade-off between relevance and diversity based on similar previously seen queries. Thorough experiments using the TREC ClueWeb09 collection show that our selective approach can significantly outperform a uniform diversification for both classical and state-of-the-art diversification approaches.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh
Authors: Santos, R.L.T., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
Research Group:Information Retrieval

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