An Adaptive e-Learning Recommender Based on User's Web-Browsing Behavior

In this study, we propose a recommender system for e-learning by utilizing a hybrid feedback method that extracts a user's preference and Web-browsing behavior. This system is capable of recommending learning content of potential interest to a user and also the likely Web-browsing action on the current item using a novel similarity measure approach. The recommender is adaptive to individual user's preference as well as one's changing interest in Web-based learning activity. A proof-of-concept system has been designed and is being implemented. Experiments are being formulated to illustrate the system's capability to acquire knowledge from user feedback and Web-browsing behavior, and to provide personalized recommendation adaptively in an e-learning environment.