Heterogeneous teaching evaluation network based offline course recommendation with graph learning and tensor factorization
Heterogeneous teaching evaluation network based offline course recommendation with graph learning and tensor factorization
复制标题
基于图学习和张量分解的异构教学评价网络离线课程推荐
DOI：
10.1016/j.neucom.2020.07.064
复制
发表时间：
2020-11-20
期刊：
影响因子：
6
通讯作者：
Niu, Zhendong
中科院分区：
文献类型：
--
作者：
Zhu, Yifan;Lu, Hao;Niu, Zhendong
课程推荐系统的应用是为了帮助不同需求的学生在大量的课程资源中选择课程。然而，学生的需求并不总是由他们的个人兴趣决定的，他们还受到老师，同伴等的影响。与在线课程不同，离线课程的用户行为和用户满意度往往存在严重的稀疏和冷启动问题，这导致了以前的神经网络和矩阵分解（MF）模型的过拟合问题。此外，人际关系、评价文本和已有的“用户-项目”格式评分矩阵构成了一个多源、多模态的数据结构，因此需要一种系统的数据融合方法来建立基于这些异质特征的推荐。因此，本文提出了一种融合图神经网络的网络结构特征和张量分解的用户交互活动的混合推荐模型。首先，提出了一个图结构的教学评价网络，通过学生的评分、评论文本、评分和人际关系来描述学生、课程等实体。然后，一个基于随机游走的神经网络，通过学习他们自己的关系结构来生成学生的矢量化表示。最后，通过将这些个性化特征识别为评分张量的第三维度，应用基于贝叶斯概率张量因子分解的张量因子分解来学习和预测学生对他们未参加的课程的评分。在一个包含532名参与者、7,453条评分记录的真实教学评价系统上的实验表明，该方法优于现有的神经网络和矩阵分解模型，包括xSVD++、RTTF和DSE，具有更小的预测误差和更好的推荐准确率。(C)2020爱思唯尔B. V.保留所有权利。
Course recommendation systems are applied to help students with different needs select courses in a large range of course resources. However, a student's needs are not always determined by their personal interests, they are also influenced by teachers, peers etc. Unlike online courses, user behavior and user satisfaction of offline courses often have serious sparse and cold start issues, which cause overfitting problems in previous neural network and matrix factorization (MF) models. Additionally, the interpersonal relations, evaluation text and existing "user-item" formatted rating matrix constitute a multi-source and multi-modal data structure, so a systematic data fusion method is needed to establish recommendations based on these heterogeneous characteristics. Therefore, a hybrid recommendation model by fusing network structured feature with graph neural networks and user interactive activities with tensor factorization was proposed in this paper. First, a graph structured teaching evaluation network is proposed to describe students, courses, and other entities by using the students' rating, commentary text, grading and interpersonal relations. Then, a random walk based neural network is employed to generate the vectorized representation of students by learning their own relational structure. Finally, by recognizing these personalization features as the third dimension of the rating tensor, a Bayesian Probabilistic Tensor Factorization-based tensor factorization is applied to learn and predict students' ratings for classes they have not taken. Experiments on a real-world evaluation of teaching system including 532 participants with 7,453 rating records show that the proposed method outperforms other existing neural network and matrix factorization models including xSVD++, RTTF and DSE with a smaller predictive error as well as better recommendation accuracy. (C) 2020 Elsevier B.V. All rights reserved.