Autodiscover: 一种基于图感知汤普森采样强化学习的推荐系统，用于主动学习中的冷启动不平衡挑战
Autodiscover: A reinforcement learning recommendation system for the cold-start imbalance challenge in active learning, powered by graph-aware thompson sampling
AI总结：
AutoDiscover通过图感知的强化学习方法，解决主动学习中的冷启动问题，提升文献综述的筛选效率。
摘要：
系统文献综述（SLR）是证据导向研究的基础，但随着科学产出的增长，手动筛选已成为日益严重的瓶颈。筛选过程中的相关研究稀缺且成本高昂。传统主动学习（AL）系统有所帮助，但通常依赖固定的查询策略来选择下一个未标记的文档。这些静态策略无法随时间适应，并且忽略了科学文献网络的关联结构。本论文介绍了一个名为AutoDiscover的框架，将主动学习重新定义为由适应性智能体驱动的在线决策问题。文献被建模为异构图，以捕捉文档、作者和元数据之间的关系。一个异构图注意力网络（HAN）学习节点表示，这些表示被Discounted Thompson Sampling（DTS）智能体用来动态管理查询策略的组合。在实时的人工在环标签下，智能体在非平稳的综述动态中平衡探索与利用，其中策略效用会随时间变化。在26个数据集的SYNERGY基准测试中，AutoDiscover在静态AL基线中实现了更高的筛选效率。关键的是，智能体通过从最小初始标签中启动发现来缓解冷启动问题，而静态方法在此处失败。我们还引入了TS-Insight，一个开源的可视化分析仪表板，用于解释、验证和诊断智能体的决策。这些贡献共同加速了在稀缺专家标签和相关研究稀缺情况下的SLR筛选。
英文摘要：
Systematic literature reviews (SLRs) are fundamental to evidence-based research, but manual screening is an increasing bottleneck as scientific output grows. Screening features low prevalence of relevant studies and scarce, costly expert decisions. Traditional active learning (AL) systems help, yet typically rely on fixed query strategies for selecting the next unlabeled documents. These static strategies do not adapt over time and ignore the relational structure of scientific literature networks. This thesis introduces AutoDiscover, a framework that reframes AL as an online decision-making problem driven by an adaptive agent. Literature is modeled as a heterogeneous graph capturing relationships among documents, authors, and metadata. A Heterogeneous Graph Attention Network (HAN) learns node representations, which a Discounted Thompson Sampling (DTS) agent uses to dynamically manage a portfolio of query strategies. With real-time human-in-the-loop labels, the agent balances exploration and exploitation under non-stationary review dynamics, where strategy utility changes over time. On the 26-dataset SYNERGY benchmark, AutoDiscover achieves higher screening efficiency than static AL baselines. Crucially, the agent mitigates cold start by bootstrapping discovery from minimal initial labels where static approaches fail. We also introduce TS-Insight, an open-source visual analytics dashboard to interpret, verify, and diagnose the agent's decisions. Together, these contributions accelerate SLR screening under scarce expert labels and low prevalence of relevant studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
