[Colloq] PhD Thesis Defense, Alan Feuer, Tuesday, Aug. 21. 10am

[Rachel Kalweit]

College of Computer and Information Science
presents
PhD Thesis Defense by:
Alan Feuer

Proposal Title:
Increasing Conversation in Proximity Search Using Phrasal Query Suggestion

Tuesday, August 21st, 2007
10:00am
366 West Village H

Abstract:
Many people have observed that searching is inherently an iterative process. Yet log studies of Web-based search engines reveal that most search sessions consist of just one or two queries, and most queries consist of just one or two words. We suggest that lack of search engine transparency and poor search engine guidance partially explain the lack of iteration.

In order to increase conversation between the searcher and the search engine, we propose a unified approach to search engine guidance based on 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 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. In addition, mean query length grew by 20% and mean session length by 30%.

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 ranking algorithm.


Committee:
Jay Aslam (advisor), 
Carole Hafner, 
Betty Salzberg 
Susan Dumais (Microsoft Research)