arXiv:2605.24155v1 Announce Type: cross Abstract: Effective skills-aware talent recommendation must balance behavioral transition patterns, trajectory-sensitive adaptation, and inspectable occupation-level criteria. Evidence from public benchmarks on how these signals interact, however, remains limited. This study proposes CF-RL-TOPSIS, an interpretable late-fusion model that integrates a transition-aware collaborative branch, a compact reinforcement-style occupation-family bandit, and an entropy-weighted TOPSIS branch constructed from six semantic proxies; the validation-selected fusion coeff
The increasing complexity of labor markets and the widespread adoption of AI in HR necessitate more sophisticated and interpretable talent recommendation systems.
This development offers a mechanism for more effective matching of skills to roles, potentially improving labor market efficiency and individual career progression.
Traditional keyword-based talent matching is being supplanted by dynamic, skills-aware, and behavior-pattern-informed AI systems.
- · HR Tech companies
- · Large enterprises with complex hiring needs
- · Individuals seeking career development
- · Outdated HR recruitment platforms
- · Companies relying solely on manual talent acquisition
Improved efficiency and accuracy in talent recruitment and internal mobility through skills-aware AI.
Potential for new platforms focusing on continuous skill development and dynamic job re-matching.
Shift towards a more fluid, skills-based labor market with reduced friction in talent allocation.
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