Discovering Users' Specific Geo Intention in Web Search

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Abstract

Discovering users’ specific and implicit geographic intention in web search can greatly help satisfy users’ information needs. We build a geo intent analysis system that uses minimal supervision to learn a model from large amounts of web-search logs for this discovery. We build a city language model, which is a probabilistic representation of the language surrounding the mention of a city in web queries. We use several features derived from these language models to: (1) identify users’ implicit geo intent and pinpoint the city corresponding to this intent, (2) determine whether the geo-intent is localized around the users’ current geographic location, (3) predict cities for queries that have a mention of an entity that is located in a specific place. Experimental results demonstrate the effectiveness of using features derived from the city language model. We find that (1) the system has over 90% precision and more than 74% accuracy for the task of detecting users’ implicit city level geo intent (2) the system achieves more than 96% accuracy in determining whether implicit geo queries are local geo queries, neighbor region geo queries or none-of these (3) the city language model can effectively retrieve cities in locationspecific queries with high precision (88%) and recall (74%); human evaluation shows that the language model predicts city labels for location-specific queries with high accuracy (84.5%).