这篇论文用DeepSeek-V3搞期刊推荐，不用训练直接匹配，23,609篇文章上Top-5超53%，比传统方法更灵活还带解释。
本文提出一种基于LLM的语义对齐框架，将期刊推荐转化为稿件内容与期刊范围描述的语义匹配问题。该框架无需任务特定训练，可直接利用LLM从标题、摘要、关键词和候选期刊信息中推断适合性。实验使用DeepSeek-V3在49个统计及相关领域期刊的23,609篇文章上进行，Top-3、Top-5和Top-10准确率分别为40.23%、53.67%和70.05%。加入参考文献信息普遍提升推荐性能，重复运行的平均Top-5 Jaccard相似度达84%，且框架能生成可解释的推理输出。
An LLM-Powered Semantic Alignment Framework for Journal Recommendation
Journal recommendation is an important task in scholarly information systems. Existing approaches typically rely on supervised learning models, manually engineered features, or historical interaction data, which may limit their generalizability and interpretability. We propose an LLM-powered semantic alignment framework that formulates journal recommendation as a semantic matching problem between manuscript content and journal scope descriptions. The framework enables large language models (LLMs) to infer journal suitability directly from article titles, abstracts, keywords, and candidate journal information without task-specific training. Experiments are conducted using DeepSeek-V3 on a dataset of 23,609 articles from 49 journals in statistics and related fields. The proposed framework achieves Top-3, Top-5, and Top-10 accuracies of 40.23\%, 53.67\%, and 70.05\%, respectively. Additional analyses show that incorporating reference information generally improves recommendation performance and that recommendations remain highly stable across repeated runs, with an average Top-5 Jaccard similarity of 84\%. The framework also generates interpretable reasoning outputs that provide insights into the recommendation process. These findings demonstrate the potential of LLMs as a training-free and scalable paradigm for journal recommendation and scholarly decision support.