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