An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation
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 coefficients remain auditable. The model is evaluated on two frozen public ICT talent-history benchmarks, JobHop and Karrierewege, using repeated chrono...
| Authors | Özkan Canay |
|---|---|
| Primary category | cs.IR |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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