PaperFlow: 跨日常论文流的画像、推荐与自适应
PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
AI总结：
提出PaperFlow框架，通过画像、推荐和自适应三阶段处理动态论文流，并构建纵向用户日基准，实验表明其在排序和行为对齐上优于基线。
摘要：
科学论文推荐通常被评估为固定候选集上的静态排序，然而真实的科研阅读是一个日常的纵向过程，其中兴趣会变化且反馈会累积。我们引入PaperFlow，一个将其组织为三个耦合阶段的框架：画像（Profiling），从异构冷启动证据中构建并维护一个结构化、可检查的学术画像；推荐（Recommending），在固定展示预算下通过多信号聚合对每个特定日期的论文流进行排序；自适应（Adapting），从语义不同的反馈信号中更新用户状态，并建模跨天的兴趣漂移。我们进一步定义了一个纵向用户日基准，该基准在共享的时间信息边界下固定用户、日期、候选池、可见输入和隐藏的模拟相关标签。该基准包含24个模拟研究用户、50个每日论文流、1200个用户日片段、20727篇独特论文和497448个片段-论文记录。我们还指定了一个盲人评估协议，以验证自动指标与专家判断之间的一致性。针对五个科学论文推荐基线的实验表明，PaperFlow实现了最强的基于oracle的排序、与模拟阅读选择最高的行为对齐以及最佳的盲人评估分数。
英文摘要：
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 three coupled stages: Profiling, which constructs and maintains a structured, inspectable scholarly profile from heterogeneous cold-start evidence; Recommending, which ranks each date-specific paper stream through multi-signal aggregation under a fixed display budget; and Adapting, which updates user state from semantically distinct feedback signals and models interest drift across days. We further define a longitudinal user-day benchmark that fixes users, dates, candidate pools, visible inputs, and hidden simulated relevance labels under a shared temporal information boundary. The benchmark contains 24 simulated research users, 50 daily paper streams, 1,200 user-day episodes, 20,727 unique papers, and 497,448 episode-paper records. We additionally specify a blind human-evaluation protocol to validate alignment between automatic metrics and expert judgments. Experiments against five scientific recommendation baselines show that PaperFlow achieves the strongest oracle-based ranking, the highest behavioral alignment with simulated reading selections, and the best blind human-evaluation score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
