An Adaptive Personalized Recommender Based on Web-Browsing Behavior Learning

In order to identify a user´s personal preference in navigating the Web or to recommend collected Web information, it is very useful to analyze the user´s Web-browsing behavior. However, it is difficult to determine which Web-browsing behaviors are influential on predicting a user´s interest because each individual has his/her own habit and personal manner in surfing the Web and locating documents of interest. In this study, we propose an adaptive personalized recommender system based on a preference-thesaurus constructed by learning a user´s Web-browsing behavior. The major components of the proposed recommender system are the Web-browsing behavior monitor, preference-thesaurus constructor, relevant document recommender, and user feedback learner. The adaptive nature of the proposed system allows the personalization of recommendation and the identification of the ever-changing influential Web-browsing behaviors. A proof-of-concept system is implemented and experiments are performed to verify the system´s capability to personalize recommendation and to learn through user´s feedbacks.