A HYBRID RECOMMENDATION MODEL FOR ONLINE LEARNING

被引：2

作者：

Hu, Xiaolu ^{[1
]}

Jiang, Tingyao ^{[1
]}

Wang, Min ^{[2
]}

机构：

[1] China Three Gorges Univ, Coll Comp & Informat Technol, Yichang 443000, Peoples R China

[2] Hubei Three Gorges Polytech, Sch Elect Informat, Yichang 443000, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION | 2022年 / 37卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Recommendation system; factorization machine; collaborative filtering; timeliness;

D O I：

10.2316/J.2022.206-0774

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：453 / 459

页数：7

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