研究人员开发了PaperFlow，一个旨在通过模仿日常阅读习惯的动态性来改进科学论文推荐的新框架。与静态排名系统不同，PaperFlow动态地剖析用户兴趣，根据日常论文流推荐论文，并随着时间的推移适应不断变化的偏好。该框架使用模拟24位用户和1200个每日片段的纵向基准进行了测试，与现有的推荐基线相比，表现出优越的性能。 AI
影响 改善研究论文的个性化发现，可能加速科学进步。
排序理由 该集群包含一篇详细介绍科学论文推荐新框架和基准的研究论文。
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arXiv:2606.07454v1 Announce Type: cross Abstract: Scientific paper recommendation is typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily, longitudinal process in which interests shift and feedback accumulates. We introd…
Scientific paper recommendation is typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily, longitudinal process in which interests shift and feedback accumulates. We introduce PaperFlow, a framework that organizes it into …
PaperFlow is a framework for scientific paper recommendation that processes user profiles, daily paper streams, and interest drift through three stages: profiling, recommending, and adapting, using a longitudinal benchmark with 24 users, 50 daily streams, and 1,200 episodes.