[Paper] PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams

Source: arXiv - 2606.07454v1

Overview

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.

Key Contributions

This paper presents research in the following areas:

• cs.IR
• cs.AI

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.IR.

Authors

• Fuqiang Wang
• Song Tan
• Zheng Guo
• Jiaohao Fu
• Xinglong Xu
• Bihui Yu
• Jie Dong
• Zheng Sun
• Siyuan Li
• Jingxuan Wei
• Cheng Tan

Paper Information

• arXiv ID: 2606.07454v1
• Categories: cs.IR, cs.AI
• Published: June 5, 2026
• PDF: Download PDF