Development of an Automated Job Search System for Students Using Machine Learning

The purpose of this work is to develop an automated system for convenient job search. An important component of the service is a recommendation system that makes it possible to provide to the user only suitable vacancies. In the course of the work, an analysis was made of existing job search services, as well as the methods used to develop recommendation systems used in various fields. As a result, a cluster approach was chosen for modeling the recommendation system, which involves dividing all the vacancies in the database into groups (clusters) and providing the user with job offers that have the same clusters as the vacancies he liked. As a result, a web service was developed that bases its work on the created recommendation system. The web service was implemented in Python with the Django framework. The hh.ru site was chosen as the source of the collection of job offers, which provides an API for receiving all current vacancies from the site and detailed information on them. The volume of thesis is 68 pages, contains 2 tables, 31 drawings. Were used 18 sources.