Abstract: To improve the employment matching and human resource utilization efficiency of college students, many researchers are dedicated to developing effective job recommendation algorithms. However, existing recommendation algorithms often rely solely on a single information source or simple user classification, which can not fully capture the multidimensional features and personalized needs of college students, resulting in poor recommendation performance. Therefore, a job recommendation algorithm for college students based on user profiles and bipartite graphs is proposed. With the aid of the conditional random field model based on the integration of long and short-term memory neural networks, the basic user information is extracted from the archives management system of the university library, based on which the user portrait of university students is generated. The distance between different user profile features is calculated, and the k-means clustering algorithm is used to complete the user profile clustering. The bipartite graph network is used to build the basic job recommendation structure for college students and a preliminary recommendation scheme is designed based on energy distribution. Finally, based on the weighted random forest model, the classification of college students’ employment positions is realized by considering users’ preferences for project features, and the score of the initial recommendation list is revised to obtain accurate recommendation results for college students’ full employment positions. The experimental results show that after the proposed method is applied, a recommendation list of 120 full employment positions for college students is given, and the hit rate of the recommendation result reaches 0. 94. This shows that the research method can accurately obtain the results of college students’ employment position recommendation, so as to improve the employment matching degree and human resource utilization efficiency. 

Key words: bipartite graph, employment positions, personalized recommendation, user profile, score correction