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With the popularization of online education and the idea of learning anytime and anywhere, more and more learners search and learn courses of various disciplines through online learning platforms to meet their personal knowledge needs. With the increase of the number of courses, it is difficult for learners to find the courses they want quickly and accurately; that is, they encounter the problems of information overload and cognitive maze. Therefore, how to recommend personalized courses for learners according to their preferences has become one of the important problems that need to be solved urgently to improve the service quality of courses in online learning platforms. Therefore, in order to improve the accuracy of course recommendation, it is necessary to build an accurate and complete learner model. In order to improve the application effect of recommendation, this paper focuses on the recommendation method of emotional factors to improve the recommendation efficiency of learning resources. The traditional recommendation model is a method based on the user’s purchase behavior and historical information. However, in the emotional factors, the effect of traditional recommendation is limited. This paper proposes a recommendation method based on emotional factors, which may consider the emotional and psychological factors of scholars according to the learning content of learners. The experimental results show that the learner model incorporating learners’ affection can reflect learners’ preferences more accurately, and the use of deep neural factorization machine for curriculum recommendation can effectively improve the accuracy of curriculum recommendation.
Yamin Wang (Fri,) studied this question.