  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.
