Abstract Summary
Recommender systems in recruitment platforms involve two active sides, candidates and recruiters, each with distinct goals and preferences. Most existing methods address only one side of the problem, leading to potential inefficient matches. We propose a two-sided fusion framework that jointly models candidate and recruiter preferences to enhance mutual matches between recruiter and candidates. Additionally, we propose a personalized two-sided fusion approach to enhance fairness job recommendation. Experiments on the XING recruitment dataset show that the proposed approach improves fairness and compatibility, demonstrating the benefits of incorporating two-sided preferences into fairness-aware recommendation.
