Extracting Interests of Users from Web Log Data Log

Abstract

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 can't accurately extract users interests. Therefore, analyzing users' Web Log Data and extracting users' potential interested domains become very important and challenging 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 and marketing which are useful for both users and Web site developers. In this paper, some novel algorithms are proposed to mine users' interests. The algorithms are based on visit time and visit density which can be obtained from an analysis of web users' Web Log Data. The experimental results of the proposed methods succeed in finding users interested domains.