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As a new channel of job seeking, online recruitment platforms and their job recommender systems have shown importance to applicants. However, existing recommendation methods endure limitation in effectiveness for their lack of consideration for employers feedback and behavioral information. Taking two-sided matching and diversity into account, this paper proposes a machine-learning based job recommendation method, namely Job-PI, to synthetically optimize both applicant preferences and employer interests. Experiments on both simulation and real-world data show the effectiveness and superiority of Job-PI over other methods.