Evaluation of phrasal query suggestions

  • Authors:

  • Alan Feuer;Stefan Savev;Javed A. Aslam

  • Affiliations:

  • Northeastern University, Boston, MA;Northeastern University, Boston, MA;Northeastern University, Boston, MA

  • Venue:
  • Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
  • Year:
  • 2007

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Abstract

This paper evaluates the uptake and efficacy of a unified approach to phrasal query suggestions in the context of a high-precision search engine. The search engine performs ranked extended-Boolean searches with the proximity operator NEAR being the default operation. Suggestions are offered to the searcher when the length of the result list falls outside predefined bounds. If the list is too long, the engine suggests narrowing the query through the use of super phrases; if the list is too short, the engine suggests broadening the query through the use of proximal subphrases. We evaluated uptake of phrasal query suggestions by analyzing search log data from before and after the suggestion feature was added to a commercial version of the search engine. We looked at approximately 1.5 million queries and found that, after they were added, suggestions represented nearly 30% of the total queries. We evaluated efficacy through a controlled study of 24 participants performing nine searches using three different search engines. We found that the engine with phrase suggestions had better high-precision recall than both the same search engine without suggestions and a search engine with a similar interface but using an Okapi BM25 ranking algorithm.