用BERT和混合模型推荐学术合作者，提升高校协作效率。
A BERT Based Hybrid Recommendation System For Academic Collaboration
- 结合BERT与TF-IDF的混合推荐方法，提升匹配精准度。
- 在相似度、聚类质量等指标上表现最优，兼顾多样性与相关性。
- 已开发移动端应用，根据技能和兴趣实时推荐合适合作者。
大学是学术合作的重要枢纽，通过跨学科对话促进师生间多元思想交流。然而，随着高校规模扩大，传统通过学生社团、班级群组和教师委员会进行人际网络构建的方式日益繁琐。为此，本文提出一种面向学术场景的个人资料推荐系统，旨在连接具有相似兴趣的校内成员。研究对比了三种技术：词频-逆文档频率（TF-IDF）、双向编码器表示（BERT）及一种混合方法，以生成有效推荐。由于数据集无标签，采用基于亲和传播聚类的重标注方法分析相似资料的分组模式。结果显示，混合模型在相似度得分、轮廓系数、Davies-Bouldin指数及归一化折损累计增益（NDCG）等多项指标上均表现更优，实现了推荐相关性与多样性的最佳平衡。此外，该最优模型已部署为移动应用，可根据用户技能与合作兴趣动态推荐相关资料，具备上下文理解能力。本系统的潜在影响显著，有望通过智能推荐系统提升大型学术机构中的协作机会。
原文摘要 · Abstract (English)
Universities serve as a hub for academic collaboration, promoting the exchange of diverse ideas and perspectives among students and faculty through interdisciplinary dialogue. However, as universities expand in size, conventional networking approaches via student chapters, class groups, and faculty committees become cumbersome. To address this challenge, an academia-specific profile recommendation system is proposed to connect like-minded stakeholders within any university community. This study evaluates three techniques: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid approach to generate effective recommendations. Due to the unlabelled nature of the dataset, Affinity Propagation cluster-based relabelling is performed to understand the grouping of similar profiles. The hybrid model demonstrated superior performance, evidenced by its similarity score, Silhouette score, Davies-Bouldin index, and Normalized Discounted Cumulative Gain (NDCG), achieving an optimal balance between diversity and relevance in recommendations. Furthermore, the optimal model has been implemented as a mobile application, which dynamically suggests relevant profiles based on users' skills and collaboration interests, incorporating contextual understanding. The potential impact of this application is significant, as it promises to enhance networking opportunities within large academic institutions through the deployment of intelligent recommendation systems.
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