# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
###### Abstract  
Personalized recommendation for online learning courses stands as a critical research topic in educational technology, where algorithmic performance directly impacts learning efficiency and user experience. To address the limitations of existing studies in multimodal heterogeneous data fusion and high-order relationship modeling, this research proposes a Heterogeneous Hypergraph and Attention-based Online Course Recommendation (HHAOCR) algorithm. By constructing a heterogeneous hypergraph structure encompassing three entity types (students, instructors, and courses), we innovatively designed hypergraph convolution operators to achieve bidirectional vertex-hyperedge information aggregation, integrated with a dynamic attention mechanism to quantify important differences among entities. The method establishes computational frameworks for hyperedge-vertex coefficient matrices and inter-hyperedge attention scores, effectively capturing high-order nonlinear correlations within multimodal heterogeneous data, while employing temporal attention units to track the evolution of user preferences. Experimental results on the MOOCCube dataset demonstrate that the proposed algorithm achieves significant improvements in NDCG@15 and F1-Score@15 metrics compared to TP-GNN (enhanced by 0.0699 and 0.0907) and IRS-GCNet (enhanced by 0.0808 and 0.0999). This work provides a scalable solution for multisource heterogeneous data fusion and precise recommendation for online education platforms. online course recommendation; heterogeneous hypergraph; attention mechanism; personalized recommendation; deep learning; graph neural networks 2020  
instructors, such as co-learning relationships among learners, course category relationships, and learners' similar preference relationships. Personalized learning is an inevitable trend in the information era [1]. Tailoring education to individual students' needs, while paying attention to their personal differences, is an important research topic in online course recommendations. Personalized recommendations on MOOC platforms intelligently match courses through user behavior analysis. For instance, when User A completes an introduction to Python course with high scores, the system recommends \"Practical Data Analysis\" based on learning paths of similar learners. If the user maintains consistent engagement during fixed evening hours (8-10 PM), live-streamed courses are prioritized. When repeated viewing of \"machine learning\" content is detected, the Fundamentals of \"Artificial Intelligence\" course is automatically highlighted on the homepage. This dynamic personalization enhances user engagement and improves course completion rates. [PERSON] et al. [2] addressed critical limitations in existing implicit feedback algorithms, including unreasonable positive-negative sample division, neglect of user operation frequency, and inaccurate user preference modeling, proposing an Implicit Feedback and Weighted User Preference-based recommendation algorithm (IFW-LFM). [PERSON] et al. [3] developed a forgetting function-enhanced Mean Bayesian Personalized Ranking (MBPR) algorithm, integrating the Ebbinghaus forgetting curve with conventional BPR methodology to achieve personalized recommendations. [PERSON] et al. [4] leveraged both low- and high-order user features through a Deep Factorization Machine architecture, combining information extraction units with cross-network structures to construct a hybrid recommendation model for deep behavioral preference mining. For sequential recommendation systems challenged by noise interference and imprecise signal representation, [PERSON] et al. [5] proposed the DeepEMA model, employing exponential moving averaging for noise reduction and trend extraction, coupled with a multi-module framework to capture diverse signals, demonstrating effectiveness across four benchmark datasets.