Intelligent Recommendation System for Music Courses Integrating Ideological and Political Education: Driven by DeepFM Algorithm
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Abstract
Personalized course recommendation in higher education requires the simultaneous consideration of learner preferences, semantic content, and educational objectives. This study proposes an intelligent music course recommendation framework based on Deep Factorization Machine (DeepFM) that incorporates ideological and educational value guidance into feature interaction and ranking optimization. Course semantics, student interests, and multidimensional value representations are jointly embedded into a unified feature space, while a value-aware attention mechanism is introduced to strengthen high-order semantic associations and improve recommendation consistency. A joint optimization strategy combining click-through prediction and value relevance further balances personalization with educational objectives. Experimental results demonstrate that the proposed model consistently outperforms conventional recommendation architectures, achieving an AUC of 0.947, an NDCG of 0.936, and a Precision@10 of 0.932 in high-value-density scenarios while maintaining stable value alignment across heterogeneous course categories. Beyond educational recommendation, the proposed multi-feature fusion and attention-driven optimization framework provides a transferable methodology for intelligent information processing and semantic decision-making, offering potential reference for knowledge management and adaptive recommendation services in electromagnetic engineering education and data-centric communication systems.
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