人类启发的任务维度引导探索：高维空间中的高效学习

中文摘要 AI 辅助

高维决策空间中的高效探索仍然是决策系统面临的核心挑战。相比之下，人类能够以显著的效率在大规模决策空间中进行导航。近期的行为研究表明，人类通过探测候选特征维度、识别与奖励相关的维度，并限制有效决策空间，从而在高维决策空间中降低维度。受这一机制的启发，我们提出了TDGE（任务维度引导探索），一种人类启发的、模型无关的算法，该算法自动构建任务维度-特征-物品的层级结构。TDGE采用自上而下的探索策略：首先选择与任务相关的特征维度，然后在这些维度内识别信息量丰富的特征，最后基于所选特征推荐具体物品。在MovieLens-20M、this http URL和Amazon推荐数据集上的实验表明，TDGE在探索效率和冷启动适应方面显著优于基线算法。与其他结构化算法的比较以及消融研究将这些优势归因于TDGE的层级结构和语义特征空间探索，且在不同聚类方法和层级深度下结果稳健。推荐轨迹可视化也显示出与人类维度引导行为相似的探索模式。

英文摘要

Efficient exploration in high-dimensional decision spaces remains a central challenge for decision-making systems. Humans, in contrast, can navigate large decision spaces with remarkable efficiency. Recent behavioral studies suggest that humans reduce dimensionality in large decision spaces by probing candidate feature dimensions, identifying reward-relevant ones, and restricting the effective decision space. Inspired by this mechanism, we propose TDGE (Task-Dimension-Guided Exploration), a human-inspired, model-agnostic algorithm with an automatically constructed task-dimension--feature--item hierarchy. TDGE follows a top-down exploration strategy: it first selects task-relevant feature dimensions, then identifies informative features within those dimensions, and finally recommends concrete items based on the selected features. Experiments on MovieLens-20M, LastFM, and Amazon recommendation datasets show that TDGE substantially improves exploration efficiency and cold-start adaptation over baseline algorithms. Comparisons with other structured algorithms and ablation studies attribute these gains to TDGE's hierarchical structure and semantic feature-space exploration, with robust results across clustering methods and hierarchy depths. Recommendation-trajectory visualizations also show exploration patterns similar to human dimension-guided behavior.

发表机构

• Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College（北京脑科学与类脑研究所，中国医学科学院 & 北京协和医学院）
• Chinese Institute for Brain Research, Beijing（北京脑科学与类脑研究中心）
• Beijing Key Laboratory of Brain Science and Brain-Machine Interface（北京市脑科学与脑机接口重点实验室）
• State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University（北京师范大学认知神经科学与学习国家重点实验室）

机构由 AI 辅助整理，请以论文原文为准。