Understanding query reformulation patterns is a key step towards next generation web search engines: it can help improving users' web-search experience by predicting their intent, and thus helping them to locate information more effectively. As a step in this direction, we build an accurate model for classifying user query reformulations into broad classes (generalization, specialization, error correction or parallel move), achieving 92% accuracy. We apply the model to automatically label two large query logs, creating annotated query-flow graphs. We study the resulting reformulation patterns, finding results consistent with previous studies done on smaller manually annotated datasets, and discovering new interesting patterns, including connections between reformulation types and topical categories. Finally, applying our findings to a third query log that is publicly available for research purposes, we demonstrate that our reformulation classifier leads to improved recommendations in a query recommendation system.

From “dango” to “japanese cakes”: query reformulation models and patterns / P. Boldi, F. Bonchi, C. Castillo, S. Vigna - In: Proceedings [of the] 2009 IEEE/WIC/ACM international conference on Web intelligence, WI 2009 : 15-18 September 2009, Università degli Studi di Milano Bicocca, Milano, Italy / [a cura di] R. Baeza-Yates, B. Berendt, E. Bertino, E.-P. Lim, G. Pasi. - Los Alamitos : IEEE Computer Society, 2009. - ISBN 9780769538013. - pp. 183-190 (( convegno International Conferences on Web Intelligence tenutosi a Milano, Italy nel 2009 [10.1109/WI-IAT.2009.34].

From “dango” to “japanese cakes”: query reformulation models and patterns

P. Boldi
Primo
;
S. Vigna
Ultimo
2009

Abstract

Understanding query reformulation patterns is a key step towards next generation web search engines: it can help improving users' web-search experience by predicting their intent, and thus helping them to locate information more effectively. As a step in this direction, we build an accurate model for classifying user query reformulations into broad classes (generalization, specialization, error correction or parallel move), achieving 92% accuracy. We apply the model to automatically label two large query logs, creating annotated query-flow graphs. We study the resulting reformulation patterns, finding results consistent with previous studies done on smaller manually annotated datasets, and discovering new interesting patterns, including connections between reformulation types and topical categories. Finally, applying our findings to a third query log that is publicly available for research purposes, we demonstrate that our reformulation classifier leads to improved recommendations in a query recommendation system.
query logs ; query reformulation ; web search
Settore INF/01 - Informatica
2009
ACM
IEEE
WIC
Book Part (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/154374
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