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The number of search queries that are associated with geographical locations, either explicitly or implicitly, has been quadrupled in recent years. For such geo-sensitive queries, the ability to accurately infer users' geographical preference greatly enhances their search experience. By mining past user clicks and constructing a geographical click probability distribution model, we address two important issues in spatial Web search: how do we determine whether a search query is geo-sensitive, and how do we detect, disambiguate, and visualize the associated geographical location(s). We present our empirical study on a large-scale dataset with about 9,000 unique queries randomly drawn from the logs of a popular commercial search engine Yahoo! Search, and about 430 million user clicks on 1.6M unique Web pages over an eight-month period. Our classification method achieved recall of 0.98 and precision of 0.75 in identifying geo-sensitive search queries. We also present our preliminary findings in using geographical click probability distributions to cluster search results for queries with geographical ambiguities.
Zhuang et al. (Tue,) studied this question.