Enhancing Academic Paper Recommendations Using Fine-Grained Knowledge Entities and Multifaceted Document Embeddings

2026-01-27

AI Summary

This study addresses the limitations of existing academic paper recommendation systems, which predominantly rely on coarse-grained topic matching and thus fail to meet scholars’ fine-grained needs for specific research methodologies or tasks. To overcome this, the authors propose a multi-dimensional document embedding approach that integrates fine-grained scientific concept entities from knowledge graphs, textual embeddings derived from paper titles and abstracts, and citation network structures to construct a precise, task-oriented recommendation model. The method introduces, for the first time, a fusion mechanism combining fine-grained knowledge entities with multi-source information. Evaluated on the STM-KG dataset, the model achieves a mean average precision of 27.3% in top-50 recommendations, representing a 6.7% improvement over current state-of-the-art methods and significantly enhancing both relevance and practical utility.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Summarization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In the era of explosive growth in academic literature, the burden of literature review on scholars are increasing. Proactively recommending academic papers that align with scholars'literature needs in the research process has become one of the crucial pathways to enhance research efficiency and stimulate innovative thinking. Current academic paper recommendation systems primarily focus on broad and coarse-grained suggestions based on general topic or field similarities. While these systems effectively identify related literature, they fall short in addressing scholars'more specific and fine-grained needs, such as locating papers that utilize particular research methods, or tackle distinct research tasks within the same topic. To meet the diverse and specific literature needs of scholars in the research process, this paper proposes a novel academic paper recommendation method. This approach embeds multidimensional information by integrating new types of fine-grained knowledge entities, title and abstract of document, and citation data. Recommendations are then generated by calculating the similarity between combined paper vectors. The proposed recommendation method was evaluated using the STM-KG dataset, a knowledge graph that incorporates scientific concepts derived from papers across ten distinct domains. The experimental results indicate that our method outperforms baseline models, achieving an average precision of 27.3% among the top 50 recommendations. This represents an improvement of 6.7% over existing approaches.

Problem

• academic paper recommendation
• fine-grained knowledge
• literature review
• research efficiency
• multifaceted document embeddings

Innovation

• fine-grained knowledge entities
• multifaceted document embeddings
• academic paper recommendation
• knowledge graph
• semantic similarity