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Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph.
- Source :
- Applied Sciences (2076-3417); Jun2024, Vol. 14 Issue 12, p5022, 18p
- Publication Year :
- 2024
-
Abstract
- This paper addresses the challenges of data sparsity and personalization limitations inherent in current recommendation systems when processing extensive academic paper datasets. To overcome these issues, the present work introduces an innovative recommendation model that integrates the wealth of structured information from knowledge graphs and refines the amalgamation of temporal and relational data. By applying attention mechanisms and neural network technologies, the model thoroughly explores the text characteristics of papers and the evolving patterns of user behaviors. Additionally, the model elevates the accuracy and personalization of recommendations by meticulously examining citation patterns among papers and the networks of author collaboration. The experimental findings show that the present model surpasses baseline models on all evaluation metrics, thereby enhancing the precision and personalization of academic paper recommendations. [ABSTRACT FROM AUTHOR]
- Subjects :
- KNOWLEDGE graphs
RECOMMENDER systems
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 14
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 178158020
- Full Text :
- https://doi.org/10.3390/app14125022