Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Geospatial mapping and navigation of the web
Proceedings of the 10th international conference on World Wide Web
Using web structure for classifying and describing web pages
Proceedings of the 11th international conference on World Wide Web
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
IEEE Intelligent Systems
Computing Geographical Scopes of Web Resources
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
User Behavior Analysis of Location Aware Search Engine
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Categorizing web queries according to geographical locality
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting geographic locations from web resources
Proceedings of the 2005 workshop on Geographic information retrieval
Spatial probabilistic modeling of calls to businesses
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Knowing the geographical serving area of web resources is very important for many web applications. Here serving area stands for the geographical distribution of online users who are interested in a given web site. In this paper, we proposed a set of novel methods to detect the serving area of web resources by analyzing search engine logs. We use the search logs to detect serving area in two ways. First, we extracted the user IP locations to generate the geographical distribution of users who had the same interests in a web site. Second, query terms input by users were considered as the user knowledge about a web site. To increase the confidence and to cover new sites for use in real-time applications, we also proposed a categorization system for local web sites. A novel method for detecting the serving area was proposed based on categorizing the web content. For each category, a radius was assigned according to previous logs. In our experiments, we evaluated all these three algorithms. From the results, we found that the approach based on query terms was superior to that based on IP locations, since search queries for local sites tended to include location words while the IP locations were sometimes erroneous. The approach based on categorization was efficient for sites of known categories and were useful for small sites without sufficient number of query logs.