Modeling user interests from web browsing activities

Browsing sessions are rich in elements useful to build profiles of user interests, but at the same time HTML pages include noisy data such as advertisements, navigation menus and privacy notes. Moreover, some pages cover several different topics making it difficult to identify the most relevant to the user. For these reasons, they are often ignored by personalized search and recommender systems. We propose a novel approach for recognizing valuable text descriptions of current user information needs-namely cues-based on the data mined from browsing interactions over the web. The approach combines page clustering techniques based on Document Object Model-based representations for acquiring evidence about relevant correlations between text contents. This evidence is exploited for better filtering out irrelevant information and facilitating the construction of interest profiles. A comparative framework proves the accuracy of the extracted cues in the personalize search task, where results are re-ranked according to the last browsed resources.