A Calibrated Multi-Agent Framework for Personalized Job Recommendation and Candidate Matching

This paper presents a calibrated multi-agent framework for personalized job recommendation and candidate matching in online employment platforms. The proposed system models the recruitment process through multiple intelligent agents responsible for candidate profiling, job understanding, preference estimation, ranking, and recommendation calibration. Unlike traditional recommendation approaches that rely solely on static similarity measures, the framework dynamically adapts to user behavior, job attributes, and evolving preferences. A calibration mechanism is introduced to improve recommendation relevance, diversity, and matching quality while reducing bias toward highly frequent job categories. The architecture is designed to be scalable and suitable for large-scale employment marketplaces, enabling efficient matching between job seekers and employers. The framework integrates behavioral signals, contextual information, and adaptive recommendation strategies to improve candidate-job alignment and user engagement. This work contributes a practical and extensible approach to AI-driven recruitment systems and provides a foundation for future research in intelligent employment platforms, recommender systems, and multi-agent decision-making.