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.</p>\n","updatedAt":"2026-06-08T03:16:48.550Z","author":{"_id":"64be296a46cc3cdfbb057f7e","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64be296a46cc3cdfbb057f7e/jSHeNY2AcPifCZzJyFhr4.jpeg","fullname":"Cheng Tan","name":"chengtan9907","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":7,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8711252808570862},"editors":["chengtan9907"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/64be296a46cc3cdfbb057f7e/jSHeNY2AcPifCZzJyFhr4.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.07454","authors":[{"_id":"6a2633c1e4c258a029492016","name":"Fuqiang Wang","hidden":false},{"_id":"6a2633c1e4c258a029492017","name":"Song Tan","hidden":false},{"_id":"6a2633c1e4c258a029492018","name":"Zheng Guo","hidden":false},{"_id":"6a2633c1e4c258a029492019","name":"Jiaohao Fu","hidden":false},{"_id":"6a2633c1e4c258a02949201a","name":"Xinglong Xu","hidden":false},{"_id":"6a2633c1e4c258a02949201b","name":"Bihui Yu","hidden":false},{"_id":"6a2633c1e4c258a02949201c","name":"Jie Dong","hidden":false},{"_id":"6a2633c1e4c258a02949201d","name":"Zheng Sun","hidden":false},{"_id":"6a2633c1e4c258a02949201e","name":"Siyuan Li","hidden":false},{"_id":"6a2633c1e4c258a02949201f","name":"Jingxuan Wei","hidden":false},{"_id":"6a2633c1e4c258a029492020","name":"Cheng Tan","hidden":false}],"publishedAt":"2026-06-05T00:00:00.000Z","submittedOnDailyAt":"2026-06-08T00:00:00.000Z","title":"PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams","submittedOnDailyBy":{"_id":"64be296a46cc3cdfbb057f7e","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64be296a46cc3cdfbb057f7e/jSHeNY2AcPifCZzJyFhr4.jpeg","isPro":false,"fullname":"Cheng Tan","user":"chengtan9907","type":"user","name":"chengtan9907"},"summary":"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. 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PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
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Abstract
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.
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.
Community
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.
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Cite arxiv.org/abs/2606.07454 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.07454 in a dataset README.md to link it from this page.
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