ACMF: An Attention Collaborative Extended Matrix Factorization Based Model for MOOC course service via a heterogeneous view

• Published in: Future generation computer systems Vol. 126; pp. 211 - 224
• Main Authors: Sheng, Deming, Yuan, Jingling, Xie, Qing, Li, Lin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2022
• Subjects: Course recommendation service; Graph representation learning; Heterogeneous information network; Matrix factorization; MOOC; Graph representation learning; MOOC; Matrix factorization; Course recommendation service; Heterogeneous information network
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2021.08.001

The spouting development of Massive Open Online Courses (MOOC) has enabled any learner to obtain abundant resource anytime and anywhere, offered a large-scale and open-access opportunity for learners to gain knowledge with great convenience. However, the surge of both learners and courses is engendering a sequence of issues, such as learner cognitive overload and messy course resources. The existing course resource service is not superior enough to suffice the dynamic and diverse demands of different individuals. Simultaneously, MOOC data suffers from sparsity and imbalance conundrums. To address those issues, we construct a heterogeneous information network (HIN) on MOOC to capture the plentiful heterogeneity between multiple entities and then propose an Attention Collaborative Extended Matrix Factorization Based Model, short as ACMF, for personalized recommendation service on MOOC course. In addition to learner and course subjects, we consider other four types of entities (e.g., knowledge, teacher, university, video) to give in-depth insights into why a learner is likely to enroll one course. Graph Convolutional Network with a novel node sampling algorithm based meta-path is utilized to learn the preserving structures and semantics in the MOOC HIN. Attention Mechanism is leveraged to automatically generate the final joint representation embedding. ACMF incorporates both explicit and implicit relationships via a heterogeneous view, integrates both learner and course embeddings via a specific semantic space. The ultimate personalized course recommendation list can be further adjusted based on learner-related or course-related through two tuning parameters. Experiments results on two real-world datasets collected from XuetangX and Junyi demonstrate the robustness and superiorities of ACMF. •Personalized MOOC course recommendation service for online learners.•An attention collaborative extended Matrix Factorization based model integrating the explicit and implicit relationship in MOOC HIN.•Further experimental comparisons of eight distinct embedding encoders and four different fusion functions on real-world MOOC data are analyzed in details.•The proposed model relieves the sparsity and imbalance issues to a certain extent and provides favorable interpretability for the recommendation results.•The ultimate personalized course recommendation list can be further adjusted based on learner-related or course-related through two tuning parameters.