A Course Recommendation Model Rooted in Heterogeneous Information Networks for Meta-Learning

Haoliang Zhang, Jingyuan Li, Kun Hou, Renrui Duan, Haiyang Jiang

Abstract: In the context of the burgeoning online education landscape, course recommendation has emerged as a pivotal element in enhancing learning efficacy and user experience. However, traditional course recommendation models often grapple with the challenges of cold start and data sparsity. To address these issues, this paper introduces a meta-learning course recommendation model based on heterogeneous information networks (HIN). The model capitalizes on the intricate structure and interconnections within HIN to learn adaptive recommendation strategies tailored to diverse user segments within the meta-learning framework. Initially, the model encodes various types of nodes and edges to construct a multi-layered HIN. It em-ploys a weighted average approach to aggregate enhanced user embedding representations derived from different meta-paths through multipath aggregation. Subsequently, the model learns initial parameters adapted to different users from a limited set of observed user behavior data. Through the optimization of the model parameters using the model-agnostic meta-learning (MAML) algorithm, personalized course recommendations are achieved. Experimental results have validated that the proposed model outperforms existing educational recommendation models in scenarios of non-cold start and data sparsity. Furthermore, when compared to the MELU model solely employing the MAML algorithm, our enhanced MEIU model integrated with multipath aggregation demonstrates a reduction of 6% in both the MAE and RMSE metrics, with a commensurate increase of 4% in the NDCG@5 metric.