Extracting Users' Interests of Web-watching Behaviors Based on Site-Keyword Graph, in A. Namatame, S. Kurihara, H. Nakashima, (Eds.), Emergent Intelligence of Networked Agents
Workshop on Emergent Intelligence of Networked Agents (WEIN 06)
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Hakodate
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Analyzing users' Web log data and extracting their interests of Web-watching behaviors are important and challenging research topics of Web usage mining. Users visit their favorite sites and sometimes search new sites by performing keyword search on search engines. Users' Web-watching behaviors can be regarded as a graph since visited Web sites and entered search keywords are connected with each other in a time sequence. We call this graph as a site-keyword graph. This paper describes a method for clarifying users' interests based on an analysis of the site-keyword graph. The method is for extracting subgraphs representing users' routine visit from a site-keyword graph which is generated from augmented Web audience measurement data (Web log data). Experimental result show that our new method succeeds in finding subgraphs which contain most of users' interested sites.
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