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

TL;DR

This paper introduces a novel academic paper recommendation system that leverages fine-grained knowledge entities and multifaceted document embeddings to improve the relevance and precision of literature suggestions for researchers.

Contribution

It proposes a new embedding-based recommendation method integrating fine-grained knowledge entities, document content, and citation data, outperforming existing models in precision.

Findings

Achieved an average precision of 27.3% among top 50 recommendations.

Outperformed baseline models with a 6.7% improvement.

Validated on the STM-KG dataset across ten scientific domains.

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.