The recruitment web platforms are a solution that has solved several problems, such as eliminating the filing of paper files to organizations, reducing recruitment time, etc. In recent years and especially with the pandemic, the job offers increased across the web with a great diversity of professional platforms and networks offering these offers. This increase in the number of job openings and the number of web platforms presents difficulties for candidates. The candidates must register and create an account on each recruiting platform. This implies a considerable waste of time for candidates to manage their accounts and to find suitable jobs according to the candidate's profile on each platform. Most recommendation systems are oriented towards the jobs platform and not towards the candidate, and depend heavily on the jobs platform. So candidates can apply on job offers specific to their specialties and skills, we propose a recommendation system for the online job offers to allow the classification of job offers to correspond to the profiles (e-portfolio) of graduates or candidates according to their skills. Our research work is carried out from extracting to calculating similarity and ranking passing by specific processing for the preparation of our data set and the classification of job offers and e-portfolios according to the skill specialty.