Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2009)
Sept. 15, 2009 to Sept. 18, 2009
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.
web search, query reformulation, query logs
P. Boldi, S. Vigna, F. Bonchi and C. Castillo, "From "Dango" to "Japanese Cakes": Query Reformulation Models and Patterns," 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Milan, Italy, 2009, pp. 183-190.