Learning path recommendation with multi-behavior user modeling and cascading deep Q networks

An online learning platform has become an important channel for learners to obtain knowledge due to its easy access and rich resources. In order to meet online learners' short-term needs with frequent changes and long-term learning goals during learning process, this paper focuses on user modeling and learning path recommendation, and we propose a new method for learning path recommendation through multi-behavior user modeling and cascading deep Q networks (cDQN-PathRec). Our model uses a knowledge graph-based multi-behavior transformer architecture for users’ state modeling, in which a learner's knowledge background, learning styles, learning settings, and learning preferences are taken into consideration. We use a cascading DQN with a two-level reward function to help an agent converge towards a balanced overall and local optima and to generate a learning path recommendation. Comprehensive experiments on two real-world online learning datasets demonstrate effectiveness of the proposed cDQN-PathRec method.