Mining Navigation Histories for User Need Recognition

The time spent using a web browser on a wide variety of tasks such as research activities, shopping or planning holidays is relevant. Web pages visited by users contain important hints about their interests, but empirical evaluations show that almost 40-50% of the elements of the web pages can be considered irrelevant w.r.t. the user interests driving the browsing activity. Moreover, pages might cover several different topics. For these reasons they are often ignored in personalized approaches. We propose a novel approach for selectively collecting text information based on any implicit signal that naturally exists through web browsing interactions. Our approach consists of three steps: (1) definition of a DOM-based representation of visited pages, (2) clustering of pages according with a tree edit distance measure and (3) exploiting the acquired evidence about the user behaviour to better filtering out irrelevant information and identify relevant text related to the current needs. A comparative evaluation shows the effectiveness of the proposed approach in retrieving additional web resources related to what the user is currently browsing