Human-Inspired, Task-Dimension-Guided Exploration for Efficient Learning and Transfer in High Dimensions

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 actively reduce dimensionality when facing large decision spaces: they probe candidate feature dimensions, rapidly identify reward-relevant ones, and use them to restrict the effective decision space. Inspired by this mechanism, we propose TDGE (Task-Dimension-Guided Exploration), a human-inspired exploration algorithm, and evaluate its performance in recommendation tasks with contextual bandit backbones. 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 three real-world recommendation datasets, MovieLens-20M, Last.fm, and Amazon, show that TDGE substantially improves exploration efficiency and cold-start adaptation over baseline algorithms. We further provide visualizations of recommendation trajectories showing that TDGE exhibits a human-like dimension-guided exploration process during learning. Code is available at https://anonymous.4open.science/r/Human-inspired-Task-Dimension-Guided-Exploration-for-Efficient-Learning-984B.