Short-Period Algorithm for Extracting the User’s Interests

The knowledge on the cobweb is growing expressively. Without a recommendation theory, the clients may come through lots of instance on the network in finding the knowledge they are stimulated in. Today, many web recommendation theories cannot give clients adequate symbolized help but provide the client with lots of immaterial knowledge. One of the main reasons is that it cannot correctly extract user's interests. Therefore, analyzing users' Web Log Data and extracting users' potential interested domains become very important research topics of web usage mining. If users' interests can be automatically detected from users' Web Log Data, they can be used for information recommendation which will be useful for both the users and the Web site developers. In this paper, one novel algorithm is proposed to extract users' interests. The algorithm is based on visit time and visit density. The experimental results of the proposed method succeed in finding user's interested domains.The knowledge on the cobweb is growing expressively. Without a recommendation theory, the clients may come through lots of instance on the network in finding the knowledge they are stimulated in. Today, many web recommendation theories cannot give clients adequate symbolized help but provide the client with lots of immaterial knowledge. One of the main reasons is that it cannot correctly extract user's interests. Therefore, analyzing users' Web Log Data and extracting users' potential interested domains become very important research topics of web usage mining. If users' interests can be automatically detected from users' Web Log Data, they can be used for information recommendation which will be useful for both the users and the Web site developers. In this paper, one novel algorithm is proposed to extract users' interests. The algorithm is based on visit time and visit density. The experimental results of the proposed method succeed in finding user's interested domains