Paper page - PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams

Source: https://huggingface.co/papers/2606.07454 Authors:

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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 recommendationis typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily,longitudinal processin 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 throughmulti-signal aggregationunder a fixed display budget; and Adapting, which updates user state from semantically distinct feedback signals and modelsinterest driftacross days. We further define a longitudinaluser-day benchmarkthat fixes users, dates, candidate pools, visible inputs, and hidden simulated relevance labels under a sharedtemporal 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 strongestoracle-based ranking, the highestbehavioral alignmentwith simulated reading selections, and the best blind human-evaluation score.

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