Efficient Recommendation Algorithm for Employment of College Students for Various Majors

Against the increasing severity of employment difficulties, job recommendation systems for college students are becoming increasingly important. As it is impossible to refer to the students’ own employment data, relevant research based on the school’s historical employment data provides a reference for students’ employment. We proposes a method based on the school’s historical employment data for job recommendation for college students. The specific ideas and characteristics are as follows: First, preprocess the collected data to generate the user portrait. In the user portrait construction process, we proposes using the AHP-Entropy Weight method to construct a weight vector of ability requirements for different positions, highlighting the focus of students’ abilities in different positions. To improve computational efficiency, first, we uses clustering algorithms to construct different user groups with different characteristics. Then, we calculates the similarity between the students to be recommended and the user groups, followed by the similarity with the users in that group to improve computational efficiency. In particular, we prove that if the number of samples in the database is greater than or equal to 6, our algorithm will have a lower average time complexity than the traditional algorithm. To address the scarcity of employment market data for college students, we collects the real employment data of graduating classes of a major in a certain university to build the HNNU-JOB data set based on students’ employment ability characteristics. Extensive experiments on HNNU-JOB show that the proposed method achieves remarkable performance of recommendation.