Implementing and evaluating phrasal query suggestions for proximity search

This paper describes and evaluates 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 NFAR 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 specializes the query through the use of super phrases; if the list is too short, the engine generalizes the query through the use of proximal subphrases. We describe methods for generating both types of suggestions and present algorithms for ranking the suggestions. Specifically, we present the problem of counting proximal subphrases for specialization and the problem of counting unordered super phrases for generalization. The uptake of our approach was evaluated 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. Efficacy was evaluated through a controlled study of 24 participants performing nine searches using three different search engines. We found that the engine with phrasal query 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. (c) 2009 Elsevier B.V. All rights reserved.