Learning to utilize auxiliary reviews for recommendation

Hyun, Dongmin; Park, Chanyoung; Cho, Junsu; Yu, Hwanjo

doi:10.1016/j.ins.2020.09.025

Cited by 16 publications

(1 citation statement)

References 3 publications

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“…Taking mental health data as an example, in the process of using by students or teachers, it is necessary to query and quote the corresponding mental health data and conduct further research and optimization on the basis of the previous research, so as to ensure the value and feasibility of the research results. However, in the actual retrieval process, it is difficult for users to accurately obtain the most valuable mental health materials in a short time [1]. In order to improve the retrieval speed of information related to the mental health of users, scholars not only recommend the methods of searching information in the network, but also solve the problems of mental health of users [2,3].…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

2022

Wireless Communications and Mobile Computing

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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.

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Section: Introductionmentioning

confidence: 99%