Cascaded Knowledge-Level Fusion Network for Online Course Recommendation System
Wenjun Ma, Yibing Zhao, Xiaomao Fan
Abstract
In light of the global proliferation of the COVID-19 pandemic, there is a notable surge in public interest towards Massive Open Online Courses (MOOCs) recently. Within the realm of personalized course-learning services, large amounts of online course recommendation systems have been developed to cater to the diverse needs of learners. However, despite these advancements, there still exist three unsolved challenges: 1) how to effectively utilize the course information spanning from the title-level to the more granular keyword-level; 2) how to well capture the sequential information among learning courses; 3) how to identify the high-correlated courses in the course corpora. To address these challenges, we propose a novel solution known as <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ascaded <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b> nowledge-level <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> usion <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork (CKFN) for online course recommendation with incorporating a three-fold approach to maximize the utilization of course information: 1) two knowledge graphs spanning from the keyword-level to title-level; 2) a two-stage attention fusion mechanism; 3) a novel knowledge-aware negative sampling method. Experimental results on a real dataset of XuetangX demonstrate that CKFN surpasses existing baseline models by a substantial margin, thereby achieving the state-of-the-art recommendation performance. It means that CKFN can be potentially deployed into MOOCs platforms as a pivotal component to provide personalized course recommendation service.