Making large-scale support vector machine learning practical
Advances in kernel methods
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
Spatial Analysis of News Sources
IEEE Transactions on Visualization and Computer Graphics
STEWARD: architecture of a spatio-textual search engine
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
LocalSavvy: aggregating local points of view about news issues
Proceedings of the first international workshop on Location and the web
Discovering geographical-specific interests from web click data
Proceedings of the first international workshop on Location and the web
Analysis of geographic queries in a search engine log
Proceedings of the first international workshop on Location and the web
Modeling and visualizing geo-sensitive queries based on user clicks
Proceedings of the first international workshop on Location and the web
International Journal of Geographical Information Science
Geographic intention and modification in web search
International Journal of Geographical Information Science
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
NICTA I2D2 group at GeoCLEF 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
TWinner: understanding news queries with geo-content using Twitter
Proceedings of the 6th Workshop on Geographic Information Retrieval
Geospatial route extraction from texts
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
Out of sight, not out of mind: on the effect of social and physical detachment on information need
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Unveiling locations in geo-spatial documents
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
The Effect of Social and Physical Detachment on Information Need
ACM Transactions on Information Systems (TOIS)
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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.