Collaborative Web Browsing Based on Semantic Extraction of User Interests with Bookmarks

With the exponentially increasing amount of information available on the World Wide Web, users have been getting more difficult to seek relevant information. Several studies have been conducted on the concept of adaptive approaches, in which the user’s personal interests are taken into account. In this paper, we propose a user-support mechanism based on the sharing of knowledge with other users through the collaborative Web browsing, focusing specifically on the user’s interests extracted from his or her own bookmarks. Simple URL based bookmarks are endowed with semantic and structural information through the conceptualization based on ontology. In order to deal with the dynamic usage of bookmarks, ontology learning based on a hierarchical clustering method can be exploited. This system is composed of a facilitator agent and multiple personal agents. In experiments conducted with this system, it was found that approximately 53.1% of the total time was saved during collaborative browsing for the purpose of seeking the equivalent set of information, as compared with normal personal Web browsing.