Heterogeneity-aware Cross-school Electives Recommendation: a Hybrid Federated Approach
This work addresses privacy and heterogeneity issues in cross-school elective recommendations for educational systems, though it appears incremental as it builds on existing federated and graph-based methods.
The paper tackles the problem of personalized elective course recommendations across schools by addressing privacy concerns and data heterogeneity, proposing HFRec, a hybrid federated recommender system that outperforms state-of-the-art models on open-source and real-world datasets.
In the era of modern education, addressing cross-school learner diversity is crucial, especially in personalized recommender systems for elective course selection. However, privacy concerns often limit cross-school data sharing, which hinders existing methods' ability to model sparse data and address heterogeneity effectively, ultimately leading to suboptimal recommendations. In response, we propose HFRec, a heterogeneity-aware hybrid federated recommender system designed for cross-school elective course recommendations. The proposed model constructs heterogeneous graphs for each school, incorporating various interactions and historical behaviors between students to integrate context and content information. We design an attention mechanism to capture heterogeneity-aware representations. Moreover, under a federated scheme, we train individual school-based models with adaptive learning settings to recommend tailored electives. Our HFRec model demonstrates its effectiveness in providing personalized elective recommendations while maintaining privacy, as it outperforms state-of-the-art models on both open-source and real-world datasets.