A recommendation algorithm for two-sided matching problems based on F-Score

According to the historical matched data and the agent´s risk preference, the proposed algorithm calculates the agent´s standardized F-Score to each agent on the opposite side. The agent on the opposite side which has the biggest standardized F-Score is recommended to the original agent. A typical application background of the algorithm is the parallel enrolment mechanism of college education in China. With contrast experiments between the proposed algorithm and other two recommendation algorithms using the real students and college admission data, it is found that the proposed algorithm has a high success recommendation rate and can respond to the agent´s risk preference.