Research on Recommendation of College Mental Health Teaching Materials Based on Improved Deep Learning Algorithm

• Published in: Wireless communications and mobile computing 2022, Vol.2022, p.1-11
• Main Authors: Zhou, Yunxia, Li, Lei
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Collaboration; College students; Colleges & universities; Deep learning; Educational materials; Health education; Machine learning; Mental health; Methods; Recommender systems; Students; Systems stability; Teaching; Textbooks
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/5184221

In order to meet the differentiated needs of students and improve the satisfaction of college mental health textbook recommendation, a college mental health textbook recommendation scheme based on improved deep learning algorithm is proposed. Based on the analysis of the principle of deep learning data recommendation system, the data interest is calculated according to the browsing records of students on college mental health textbooks. Combine the deep learning algorithm and collaborative filtering algorithm to collect the demand data of college mental health textbooks, then use the Naive Bayesian classification method to divide the college mental health textbooks into interested and uninterested parts, and recommend the interested college mental health textbooks to the students in need. Experiments show that the longest recommendation time of the college mental health textbook recommendation scheme based on the improved deep learning algorithm proposed in this paper is 4.5 min, the highest recommended recall rate is 95.18%, the average accuracy is 97.2%, the highest content richness is 0.8, the system stability coefficient is 1.06, and the overall average praise rate is 97.79%. It has a good recommendation effect.