PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
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 longit...
| Authors | Fuqiang Wang, Song Tan, Zheng Guo et al. |
|---|---|
| Primary category | cs.IR |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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