Dual Representation of the Semantic User Profile for Personalized Web Search in an Evolving Domain

Several semantic based user profile approaches have been introduced in the literature to learn the users’ interests for personalized search. However, many of them are ill-suited to cope with a domain of information that evolves and user interests that may change over time. In this paper, we propose a novel dual representation of a user’s semantic profile to deal with this problem: (1) a lower-level semantic representation, consisting of an accumulated gathering of user activities over a long period of time, that uses a standard machine learning algorithm to detect user convergence, (2) a higher-level semantic representation that detects shifts in the user activitiesonce this shift is detected, the higher-level semantic representation automatically updates the user profiles and reinitialize the system. Our experimental results demonstrate the feasibility of this approach.