SkillGATE improves skill retrieval accuracy using tree search and graphs
SkillGATE: Gate-Aware Monte Carlo Tree Search for Skill Retrieval
Artificial Intelligence
Summary
Finding the right skills from big skill libraries can be tricky because many skills can seem similar and tools can get confused. The authors treat this search like a smart exploration process that decides where to look based on what it learns along the way. They propose SkillGATE, which uses a clever search method guided by a skill graph to find better matches step-by-step. Their tests show that SkillGATE finds the right skills more often than existing methods.
What this means in practice
- •For enterprise software developers: Improve enterprise skill management tools by integrating SkillGATE for more accurate retrieval from large skill databases.
- •For human resources teams: Enhance talent and skill matching platforms to better identify relevant employee skills from expansive skill inventories.
Authors
Rongchen Zhao, Yu Chen, Yanming Yang, Shijia Xu, Juyuan Wang, Jin Xu, Zibin Zheng, Jingping Liu
Abstract
Skill Retrieval (SR) aims to identify the most relevant skills from external skill libraries, and becomes increasingly challenging as libraries grow in scale and diversity. Existing methods either rank skills independently or rely on predefined graph propagation and hierarchical routing, making them vulnerable to semantic distractors, local trapping, and early routing errors. We formulate SR as an adaptive information-foraging process that coordinates region-level navigation with skill-level selection according to the utility and uncertainty observed during search. Based on this formulation, we propose SkillGATE, a graph-guided hierarchical retrieval framework with Gate-Aware Monte Carlo Tree Search (MCTS). SkillGATE constructs a graph-preserving hierarchical index and performs adaptive retrieval through selection, expansion, simulation, and backpropagation. G-PUCT guides action selection, expansion explores new regions, simulation evaluates candidate skills, and backpropagation updates search statistics. Experiments on six SR benchmarks show that SkillGATE consistently improves diverse retrieval and reranking backbones, achieving a 16.3\% improvement in overall R@1 over the strongest retriever-based baseline. Our code is available at https://github.com/Edwinbe/SkillGATE-v1/.