Segment-Aware LorentzFM: Hyperbolic embedding approach for personalized job recommendations

Job recommendation systems face two critical challenges: treating users as a homogeneous group despite diverse job-seeking behaviors, and inadequately modeling the hierarchical nature of job markets. This study proposes Segment-Aware LorentzFM, a novel framework combining behavioral user segmentation with Lorentz-model hyperbolic embeddings. We analyze 1.6 million job applications from 103,896 users on Kariyer.net, revealing three distinct behavioral segments through unsupervised clustering: Ideal Candidates (38.1%) with focused patterns, Career Explorers (51.4%) with diverse behaviors, and Balanced Seekers (10.5%) with moderate exploration. We establish a theoretical foundation connecting segmentation with hyperbolic geometry, proving optimality through information-theoretic and geometric perspectives. Our triangle scoring mechanism in hyperbolic space models interactions among user characteristics, job attributes, and reference points, naturally encoding job market hierarchies. Experimental results demonstrate that Segment-Aware LorentzFM achieves AUC-ROC of 0.9718, outperforming the best baseline by 5.5% and original LorentzFM by 9.4%. The model shows exceptional cold-start performance with F1-score of 0.6068 for new users and jobs. Key contributions include: (1) first framework connecting behavioral segmentation with hyperbolic geometry, (2) novel triangle scoring function in Lorentz space, (3) significant improvements in both warm-start and cold-start scenarios, and (4) practical deployment guidelines. This work advances job recommendation systems by combining user behavioral understanding with geometric representation learning.