MOOCs video recommendation using low-rank and sparse matrix factorization with inter-entity relations and intra-entity affinity information

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yunmei Gao
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引用次数: 0

Abstract

Purpose

The serious information overload problem of MOOCs videos decreases the learning efficiency of the students and the utilization rate of the videos. There are two problems worthy of attention for the matrix factorization (MF)-based video learning resource recommender systems. Those methods suffer from the sparsity problem of the user-item rating matrix, while side information about user and item is seldom used to guide the learning procedure of the MF.

Method

To address those two problems, we proposed a new MOOCs video resource recommender LSMFERLI based on Low-rank and Sparse Matrix Factorization (LSMF) with the guidance of the inter-Entity Relations and intra-entity Latent Information of the students and videos. Firstly, we construct the inter-entity relation matrices and intra-entity latent preference matrix for the students. Secondly, we construct the inter-entity relation matrices and intra-entity affinity matrix for the videos. Lastly, with the guidance of the inter-entity relation and intra-entity affinity matrices of the students and videos, the student-video rating matrix is factorized into a low-rank matrix and a sparse matrix by the alternative iteration optimization scheme.

Conclusions

Experimental results on dataset MOOCcube indicate that LSMFERLI outperforms 7 state-of-the-art methods in terms of the HR@K and NDCG@K(K = 5,10,15) indicators increased by an average of 20.6 % and 21.0 %, respectively.

利用具有实体间关系和实体内亲和力信息的低秩稀疏矩阵因式分解进行 MOOC 视频推荐
MOOCs 视频存在严重的信息过载问题,降低了学生的学习效率和视频的利用率。基于矩阵因式分解(MF)的视频学习资源推荐系统有两个问题值得关注。这些方法存在用户-项目评分矩阵的稀疏性问题,而用户和项目的侧面信息很少用于指导矩阵因式分解的学习过程。针对这两个问题,我们提出了一种基于低秩稀疏矩阵因式分解(LSMF)、以学生和视频的实体间关系和实体内潜在信息为指导的新型 MOOCs 视频资源推荐器 LSMFERLI。首先,我们构建学生的实体间关系矩阵和实体内潜在偏好矩阵。其次,我们构建视频的实体间关系矩阵和实体内亲和矩阵。最后,在学生和视频的实体间关系矩阵和实体内亲和矩阵的指导下,通过替代迭代优化方案将学生-视频评分矩阵因式分解为低秩矩阵和稀疏矩阵。在数据集 MOOCcube 上的实验结果表明,LSMFERLI 在 HR@ 和 NDCG@( = 5,10,15) 指标上优于 7 种最先进的方法,平均增幅分别为 20.6% 和 21.0%。
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
期刊介绍: Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.
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