Learning to rank query suggestions for adhoc and diversity search

  • Authors:

  • Rodrygo L. Santos;Craig Macdonald;Iadh Ounis

  • Affiliations:

  • School of Computing Science, University of Glasgow, Glasgow, UK G12 8QQ;School of Computing Science, University of Glasgow, Glasgow, UK G12 8QQ;School of Computing Science, University of Glasgow, Glasgow, UK G12 8QQ

  • Venue:
  • Information Retrieval
  • Year:
  • 2013

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Abstract

Query suggestions have become pervasive in modern web search, as a mechanism to guide users towards a better representation of their information need. In this article, we propose a ranking approach for producing effective query suggestions. In particular, we devise a structured representation of candidate suggestions mined from a query log that leverages evidence from other queries with a common session or a common click. This enriched representation not only helps overcome data sparsity for long-tail queries, but also leads to multiple ranking criteria, which we integrate as features for learning to rank query suggestions. To validate our approach, we build upon existing efforts for web search evaluation and propose a novel framework for the quantitative assessment of query suggestion effectiveness. Thorough experiments using publicly available data from the TREC Web track show that our approach provides effective suggestions for adhoc and diversity search.