Implementation of School-Enterprise Two-Sided Matching Based on Pearson and Top Trading Cycle

Due to a large number of graduates and employment information, two-sided matching is increasingly used in employment. However, the general recruitment websites are not rich in student data, so their recommendation will not be very accurate. In order to improve the efficiency of recommendation, a recommendation system based on Pearson and Top Trading Cycle is designed by using the real graduate data of our school. Through the application of Pearson and TTC (Top Trading Cycle), the stable match between the students and the enterprise is realized. We use Pearson similarity algorithm to calculate the similarity between students and sort them from large to small, then we arrange the similarity of order as user preferences in TTC algorithm. Take each of the students as the starting point and the end point, the similarity as the evaluation criteria, and the rest of the students to form a ring. We believe that all the enterprises of the past students in the ring area are the recommended enterprises to be allocated to the students in the ring area, and the students who are to be assigned are recommended students recommended by them.