An Improved E-Learner Communities Self-organizing Algorithm Based on Hebbian Learning Law

摘要

In this paper we propose an improved E-Learner communities self-organizing algorithm based on Hebbian Learning Law, which can automatically group distributed e-learners with similar interests and make proper recommendations. Through similarity discovery, trust weights update and potential neighbors adjustment, the algorithm implements an automatic-adapted trust relationship with gradually enhanced satisfactions. It avoids difficult design work required for user preference representation or user similarity calculation. Hence it is suitable for open and distributed e-learning environments. Experimental results have shown that the algorithm has preferable prediction accuracy and user satisfaction. In addition, we achieve an improvement on both satisfaction and scalability.