A trust-personality mechanism for emotion compensation

E-learning provides an unprecedented flexibility and convenience for learners via breaking the limitation of spacetime. Most researchers are only concerned about the learner's cognitive and construct a great amount of substantive digital learning resources, however they neglect of the learners' affect in current e-learning systems. In this paper, we focus primarily on the negative affect of learners, and propose an emotion compensation mechanism associated with trust and personality traits in traditional recommender technology. First, we analyze the difference between emotion compensation and traditional recommender. Next, the score of trust is calculated with historical behavior; otherwise depend on similarity of personality traits without historical experience. We use trustworthiness to replace similarity as prediction weight in trust filtering process. At last we do experiments with data collected in previous system named emotion-chatting. Compared with results of experiments between traditional recommender and trust-personality recommender, the average of accuracy is improved 4 points in percentage.