Selectively diversifying web search results

  • Authors:
  • Rodrygo L.T. Santos;Craig Macdonald;Iadh Ounis

  • Affiliations:
  • University of Glasgow, Glasgow, United Kingdom;University of Glasgow, Glasgow, United Kingdom;University of Glasgow, Glasgow, United Kingdom

  • Venue:
  • CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

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.