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Eugene Agichtein
Researcher at Emory University
Publications - 166
Citations - 11564
Eugene Agichtein is an academic researcher from Emory University. The author has contributed to research in topics: Question answering & Web search query. The author has an hindex of 47, co-authored 166 publications receiving 10917 citations. Previous affiliations of Eugene Agichtein include Amazon.com & Microsoft.
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Proceedings ArticleDOI
Ready to buy or just browsing?: detecting web searcher goals from interaction data
Qi Guo,Eugene Agichtein +1 more
TL;DR: This work presents a new class of search behavior models that also exploit fine-grained user interactions with the search results, and shows that mining these interactions can enable more effective detection of the user's search goals.
Proceedings ArticleDOI
Learning search engine specific query transformations for question answering
TL;DR: A method for learning query transformations that improves the ability to retrieve answers to questions from an information retrieval system and presents a prototype search engine, Tritus, that applies the method to web search engines.
Proceedings ArticleDOI
The influence of caption features on clickthrough patterns in web search
TL;DR: The findings of this study suggest that relatively simple caption features such as the presence of all terms query terms, the readability of the snippet, and the length of the URL shown in the caption, can significantly influence users' Web search behavior.
Proceedings ArticleDOI
Towards predicting web searcher gaze position from mouse movements
Qi Guo,Eugene Agichtein +1 more
TL;DR: The first results on automatically inferring whether the searcher's gaze position is coordinated with the mouse position are reported - a crucial step towards predicting the searchers gaze position from the computer mouse movements.
Proceedings ArticleDOI
Find it if you can: a game for modeling different types of web search success using interaction data
TL;DR: This work proposes a principled formalization of different types of "success" for informational search, which encapsulate and sharpen previously proposed models, and presents a scalable game-like infrastructure for crowdsourcing search behavior studies, specifically targeted towards capturing and evaluating successful search strategies on informational tasks with known intent.