A case study of using geographic cues to predict query news intent.

Geographic information retrieval encompasses important tasks including finding the location of a user, and locations relevant to their search queries. Web-based search engines receive queries from numerous users located in very different parts of the world. A typical way for people to find news is through a general web search engine, which makes it important for search engines to recognize queries with news intent. An important question for geographic information retrieval is how we can benefit from geographic cues to predict the intent of users. This work presents a case study of an application using geographic features to improve the quality of an important web search task, involving predicting which queries have news intent and hence are likely to receive clicks on news search results. Our case study suggests that information derived from geographic features can help the task. The information we consider includes cues derived from the location of the user, from the IP address, the location relevant to the query, automatically extracted from the query string, and the relation between the two locations. We build a classifier that uses geographical cues to predict whether a query will result in a news click or not. We compare our classifier to a strong baseline that use non-geographic click-based features and we show that our classifier outperforms the baseline for geographic queries.