报告题目：Academic Literature Recommendation in Large-scale Citation Networks Enhanced by Large Language Models

报告时间：2025年6月23日下午15：30

报告地点：南湖校区新图书馆5302室

主办单位：欧洲杯

报告人：潘蕊

报告人简介：潘蕊，中央财经大学统计与数学学院教授、博士生导师，中央财经大学龙马学者青年学者。主要研究领域为网络结构数据的统计建模、时空数据的统计分析等。在Annals of Statistics、Journal of the American Statistical Association、Journal of Business & Economic Statistics等期刊发表论文30余篇。著有中文专著《数据思维实践》、《网络结构数据分析与应用》。主持国家自然科学基金项目、全国统计科学研究项目等。

摘要：Literature recommendation is essential for researchers to find relevant articles in an ever-growing academic field. However, traditional methods often struggle due to data limitations and methodological challenges. In this work, we construct a large citation network and propose a hybrid recommendation framework for scientific article recommendation. Specifically, the citation network contains 190,381 articles from 70 journals, covering statistics, econometrics, and computer science, spanning from 1981 to 2022. The recommendation mechanism integrates network-based citation patterns with content-based semantic similarities. To enhance content-based recommendations, we employ text-embedding-3-small model of OpenAI to generate an embedding vector for the abstract of each article. The model has two key advantages: computational efficiency and embedding stability during incremental updates, which is crucial for handling dynamic academic databases. Additionally, the recommendation mechanism is designed to allow users to adjust weights according to their preferences, providing flexibility and personalization. Extensive experiments have been conducted to verify the effectiveness of our approach. In summary, our work not only provides a complete data system for building and analyzing citation networks, but also introduces a practical recommendation method that helps researchers navigate the growing volume of academic literature, making it easier to find the most relevant and influential articles in the era of information overload.