A HYBRID RECOMMENDATION MODEL FOR ONLINE LEARNING

Xiaolu Hu; Tingyao Jiang; Min Wang

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A HYBRID RECOMMENDATION MODEL FOR ONLINE LEARNING

机译：A HYBRID RECOMMENDATION MODEL FOR ONLINE LEARNING

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摘要

In online learning platforms, personalized course recommendation services can significantly improve users' interest and learning efficiency. A hybrid recommendation model, which comprehensively takes the timeliness of courses and the implicit interests of students into consideration, is proposed to improve the diversity of recommendation methods and solve the problem of data sparsity in the existing online learning platforms. Experiments on the massive open online course (MOOC) dataset of Chinese universities show that the proposed model has improved the performance of course recommendations to a certain extent without excessively increasing the algorithm's complexity.

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来源
《International Journal of Robotics & Automation》 |2022年第5期|453-459|共7页
作者
Xiaolu Hu; Tingyao Jiang; Min Wang;
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作者单位

College of Computer and Information Technology, China Three Gorges University;

School of Electronic Information, Hubei Three Gorges Polytechnic;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类机器人技术;
关键词
Recommendation system; Factorization machine; Collaborative filtering; Timeliness;

A HYBRID RECOMMENDATION MODEL FOR ONLINE LEARNING

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