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
- Ready to Buy or Just Browsing? Detecting
Web Searcher Goals from Interaction Data,
Q. Guo and E. Agichtein, Proc. of SIGIR 2010
Predicting Web Searcher Gaze Position from Mouse Movements,
Q. Guo and E. Agichtein, In Extended Abstracts, Proc. of CHI 2010
Query Ambiguity Revisited: Clickthrough Measures for Distinguishing
Informational and Ambiguous Queries,
Y. Wang and E. Agichtein, in Proc. of (NAACL-HLT), 2010
Searcher Interactions for Distinguishing Types of Commercial Intent
Q. Guo, and E. Agichtein, in Proc. of WWW 2010
In the Mood to
Click? Towards Inferring Searcher Receptiveness to Advertising
Q. Guo, E. Agichtein, C. Clarke and A. Ashkan
Proc. of the ACM/IEEE International Conference on Web Intelligence (WI),
EMU: The Emory
User Behavior Modeling System for Automatic Library Search Evaluation:
Preliminary Results (poster),
Qi Guo, Ryan P. Kelly, Selden Deemer, Arthur Murphy, Joan A. Smith, and
Proc. of the 9th Joint Conference on Digital Libraries (JCDL), 2009
Classifying and Characterizing Query Intent in Sponsored Search,
Azin Ashkan, Charles Clarke,
Eugene Agichtein, and Qi Guo,
Proc. of the 31st European Conference on Informational Retrieval (ECIR),
Understanding "Abandoned" Ads: Towards
Personalized Commercial Intent Inference via Mouse Movement Analysis,
Qi Guo, Eugene Agichtein, Charles Clarke and Azin Ashkan,
SIGIR 2008 Workshop on Information Retrieval in Advertising (IRA),
Characterizing Query Intent
From Ad Clickthrough Data,
Azin Ashkan, Charles Clarke,
Eugene Agichtein and Qi Guo,
2008 Workshop on Information
Retrieval in Advertising (IRA),
Instrumentation for Personalized
Search Intent Inference:
Qi Guo and Eugene Agichtein,
Workshop on Intelligent
Techniques for Web
Personalization and Recommender
Mouse Movements for Inferring Query Intent (poster)
Qi Guo and Eugene Agichtein
Proc. of the 31st Annual International ACM SIGIR Conference (SIGIR 2008)
Research supported by: