Modeling and Inferring Searcher Intent by Mining User Interactions

News and Updates

  • 5/23/2013: Code and Data for SIGIR 2013 Paper “Improving Search Result Summaries by Using Searcher
    Behavior Data”
    is available here .

  • 7/15/2011: Paper on remote studies of search result examination appeared in SIGIR 2011. More details here:
    Paper, citation information, demo, and data.
  • 7/1/2011: Paper on modeling types of search success via search contest data appeared in SIGIR 2011. More details here: Paper, citation information, data, and code.
  • 7/1/2010: New Scientist wrote a brief news blurb and a feature story about our SIGIR 2010 paper.

    Inferring searcher intent is a central problem in information retrieval and web search: for effective ranking and result presentation, the search engine must know what the user is looking for. Yet, expressing a searcher information need currently relies on entering the .right. search keywords, which can require multiple rounds of trial-and-error from the searcher. The goal of this project is to develop effective methods for a search engine to automatically infer searcher intent and information needs from the searcher interactions and behavior data. Specifically, the project addresses two main challenges of search intent inference: developing accurate and robust models of searcher intent and behavior, and exploiting these models to infer search intent for each individual user. This project significantly advances previous efforts on implicit feedback and search modeling, by considering a wide range of user interaction and contextual features, and by developing novel techniques for mining and exploiting these signals to improve web search and information access.

    Tools and Datasets

  • Code and Data for Improving Search Result Summaries by Using Searcher
    Behavior Data [Ageev, Lagun, and Agichtein, SIGIR 2013].

  • Code and Data for the UFindIt game for modeling search success [Ageev, Guo, Lagun, and Agichtein, SIGIR 2011].
  • Code and data for ViewSer, a system for remote studies of search result examinationb [Lagun and Agichtein, SIGIR 2011]


    • Eugene Agichtein, Director of the Emory IR Lab, and Associate Professor, Emory University
    • Dr. Mikhail (Misha) Ageev, Moscow State University (Visiting scholar at Emory in 2011, and 2012-2013).
    • Dmitry Lagun, Ph.D. Student (2009-)
    • Dr. Qi Guo , graduated with Ph.D. in November 2012, currently Applied Research Scientist at Microsoft Bing.
    • Charles L Clarke, University of Waterloo, commercial intent project, 2007-2009


    Research supported by: