PaperFlow

PaperFlow: A longitudinal framework for daily scientific paper recommendation with profiling and interest drift

PaperFlow is a new framework for scientific paper recommendation that models the process as a longitudinal, daily workflow rather than a static ranking task. It comprises three coupled stages: Profiling (building user scholarly profiles from cold-start evidence), Recommending (ranking daily paper streams under a display budget), and Adapting (updating user state from feedback and modeling interest drift). The authors introduce a benchmark with 24 simulated users, 50 daily paper streams, and over 1.2 million episode-paper records, plus a blind human-evaluation protocol. PaperFlow outperforms five baselines on oracle ranking, behavioral alignment, and human evaluation.