User profiling for interest-focused browsing history

User profiling is an important part of the Semantic Web as it integrates the user into the concept of Web data with machine-readable semantics. In this paper, user profiling is presented as a way of providing the user with his/her interest-focused browsing history. We present a system that is incorporated into the Internet Explorer and maintains a dynamic user profile in a form of automatically constructed topic ontology. A subset of previously visited Web pages is associated with each topic in the ontology. By selecting a topic, the user can view the set of associated pages and choose to navigate to the page of his/her interest. Each topic can be seen as an interest of the user (hence the term interest-focused browsing history). The ontology is constructed by transforming the textual contents of the pages into sparse word-vectors and applying bisecting k-means clustering (ie a form of hierarchical clustering) on the set of sparse vectors. The most recently visited pages are used to identify the user’s current interest and map it to the ontology. The user can clearly see which topics, and their corresponding pages, are related (or are not related, for that matter) to his/her current interest. We see this as a useful way of organizing the user’s browsing history. To illustrate the functioning of the system, we demonstrate its behavior in one particular real-life scenario.