As the Internet continues to play an important role in many business applications, it becomes vital to increase the competitive edge by offering geographically tailored contents that reflect the common interests of the geographica

As the Internet continues to play an important role in many business applications, it becomes vital to increase the competitive edge by offering geographically tailored contents that reflect the common interests of the geographical region of the web visitors. In this paper, we define the problem of mining geographical-specific interests patterns. We utilize the quadtree to model the influence distributions of different features, and design an algorithm called Flex-iPROBER to mine geographical-specific interests patterns that are significant in a local region. We further examine how these patterns can change over time and develop an algorithm called MineGIC to efficiently discover pattern changes. Experiment results demonstrate that the proposed algorithms are scalable and efficient. Patterns discovered from real world web click datasets reveal interesting patterns and show the evolution of the interests of people in those regions.

As the Internet continues to play an important role in many business applications, it becomes vital to increase the competitive edge by offering geographically tailored contents that reflect the common interests of the geographi

As the Internet continues to play an important role in many business applications, it becomes vital to increase the competitive edge by offering geographically tailored contents that reflect the common interests of the geographical region of the web visitors. In this paper, we define the problem of mining geographical-specific interests patterns. We utilize the quadtree to model the influence distributions of different features, and design an algorithm called Flex-iPROBER to mine geographical-specific interests patterns that are significant in a local region. We further examine how these patterns can change over time and develop an algorithm called MineGIC to efficiently discover pattern changes. Experiment results demonstrate that the proposed algorithms are scalable and efficient. Patterns discovered from real world web click datasets reveal interesting patterns and show the evolution of the interests of people in those regions.