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Human-inspired, Task-Dimension-Guided Exploration for Efficient Learning in High Dimensions

arXiv 2609.36672 ↗First posted 29 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 15

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  11. 2023Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?cited by 36
  12. 2022Contextual Bandits with Large Action Spaces: Made Practicalcited by 33
3 more
  1. 2020High-Dimensional Bayesian Optimization via Tree-Structured Additive Modelscited by 22
  2. 2021A Survey on Reinforcement Learning for Recommender Systemscited by 18
  3. 2023High-dimensional Bayesian Optimization with Group Testingcited by 5

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