An energy algorithm of negative emotion compensation in e-learning

Focusing on the learner's negative emotions, this paper proposes an energy algorithm to regulate negative emotions in e-learning, which develops the recommendation based on trust, personality and item similarity. According to historical interactive behaviors, we present a computational model to establish the trust network, and the expression of personality and items to evaluate the similarity in negative emotion compensation. Experimental results show that the proposed algorithm gives higher accuracy and a better learner's satisfaction.