A Multi-agent Framework for Context-Aware Dynamic User Profiling for Web Personalization

Growing volume of information on World Wide Web has made relevant information retrieval a difficult task. Customizing the information according to the user interest has become a need of the hour. Personalization aims to solve many associated problems in current Web. However, keeping an eye on user's behavior manually is a difficult task. Moreover, user interests change with the passage of time. So, it is necessary to create a user profile accurately and dynamically for better personalization solutions. Further, the automation of various tasks in user profiling is highly desirable considering large size and high intensity of users involved. This work presents an agent-based framework for dynamic user profiling for personalized Web experience. Our contribution in this work is the development of a novel agent-based technique for maintaining long-term and short-term user interests along with context identification. A novel agent-based approach for dynamic user profiling for Web personalization has also been proposed. The proposed work is expected to provide an automated solution for dynamic user profile creation.