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.

翻译：在学术文献爆发式增长的时代，学者进行文献综述的负担日益加重。在研究过程中，主动推荐符合学者文献需求的学术论文，已成为提升研究效率和激发创新思维的关键途径之一。当前的学术论文推荐系统主要基于宽泛的主题或领域相似性进行粗粒度的推荐。这些系统虽能有效识别相关文献，但难以满足学者更具体、更细粒度的需求，例如定位使用特定研究方法或处理同一主题下不同研究任务的论文。为满足学者研究过程中多样且具体的文献需求，本文提出了一种新颖的学术论文推荐方法。该方法通过整合新型细粒度知识实体、文档标题与摘要以及引文数据，对多维信息进行嵌入表示，进而通过计算组合后的论文向量之间的相似性来生成推荐。所提出的推荐方法使用STM-KG数据集进行评估，该知识图谱整合了来自十个不同领域论文的科学概念。实验结果表明，我们的方法优于基线模型，在Top-50推荐中实现了27.3%的平均精确率，相比现有方法提升了6.7%。