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- Title
SSRES: A Student Academic Paper Social Recommendation Model Based on a Heterogeneous Graph Approach.
- Authors
Guo, Yiyang; Zhou, Zheyu
- Abstract
In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments.
- Subjects
CONVOLUTIONAL neural networks; BIG data; INDIVIDUALIZED instruction; INFORMATION overload; RECOMMENDER systems; SOCIAL media; SOCIAL networks
- Publication
Mathematics (2227-7390), 2024, Vol 12, Issue 11, p1667
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math12111667