周波数領域と時間領域におけるハイブリッド注意メカニズムに基づくMOOCコースレコメンデーションシステムの研究

Course recommendation systems serve as a critical component of online education platforms, playing a vital role in enhancing learning efficiency and personalized experiences. However, existing recommendation approaches, including recent sequential models such as BERT4Rec and LightSANs, primarily concentrate on temporal-domain modeling of user behaviors while neglecting the potential of frequency-domain analysis. This leads to incomplete characterization of user behavior patterns, particularly presenting challenges in capturing stable long-term interests from sparse and noisy interaction data. To address these limitations, this study proposes a novel hybrid attention network for Massive Open Online Courses Course recommendation, designed to jointly model both frequency-domain and temporal-domain features. The model employs Fast Fourier Transform to extract frequency-domain characteristics from user behavior sequences while utilizing a self-attention mechanism to capture temporal dynamics, thereby enabling collaborative modeling of dual-domain features. Experimental results on the public MooCCube dataset demonstrate that the proposed model achieves Hit Ratio@10, MRR@10, and NDCG@10 scores of 0.4534, 0.2018, and 0.2618, respectively, outperforming current mainstream recommendation algorithms. Ablation studies further validate the effectiveness of dual-domain fusion, with approximately 10% and 5% performance improvements in NDCG@10 and Hit@10 compared to single-domain approaches. This research provides a novel technical pathway for overcoming performance bottlenecks in personalized course recommendation.