Design and implementation of student job matching system based on personalized recommendation algorithm

In the current job matching of students in higher vocational colleges, the traditional recommendation system often faces some problems, such as inaccurate job information matching, insufficient personalization of recommendation and poor user experience. This paper studies the design and implementation of student job matching system based on personalized recommendation algorithm, aiming at improving students' job matching degree and satisfaction through systematic methods. Firstly, the basic principle of personalized recommendation algorithm, including collaborative filtering and content recommendation algorithm, is discussed, and combined with the employment characteristics of students in higher vocational colleges, the job matching needs are analyzed. It is pointed out that higher vocational students are faced with limited employment opportunities and unequal skill level, so the recommendation system needs to meet the diversification of job information, personalized management of student data and ease of use of the system. When conducting user behavior analysis, it was observed that in a total of 598 user interactions, the average dwell time per user was 3.5 minutes and the number of page views reached 4.506. This indicates that the user's interest in the content is higher. The statistics of user activity show that the conversion rate is 70.2%, which shows strong user engagement. There are a total of 523 active events in the system, of which 38 events have significant user interaction frequencies. Finally, this paper provides in-depth theoretical basis and practical guidance for the application of personalized recommendation algorithm in the field of employment matching.