A Prototype for an e-Recruitment Platform using Semantic Web Technologies

Abstract

1 IntroductionSince the late 1990s when the labour market faced important economy changes and had a high demand for qualified candidates, online web recruitment domain has grown steadily, surpassing the traditional methods. An e-Recruitment solution offers a significant gain in terms of efficiency by speeding up the recruitment process and saving time to all those involved. A job recommender system is supposed to help the recruiters, by recommending the most eligible candidates for a particular job and, on the other hand, to propose jobs to the aspiring candidates, matching their profiles with the existing job offers.It is difficult for a candidate to analyse all the open positions from one or more companies and then select from them those that really fit his profile. In many cases it can occur that, although the candidate possesses all the required abilities for a job, he may not be aware of the job existence. Automatic matching between the skills and abilities that a company requires for a specific job and those an applicant has, helps decrease the errors that can occur when the process is done manually (having in mind the huge amount of CVs that need to pass by a screening process before they are deep checked). What is important to note is that these e-Recruitment platforms are assisting and not replacing the recruiters in their decision-making process.Moreover, using an online recruitment system to connect people with job opportunities has also other benefits. For example, it provides all the needed information to do a comprehensive analysis about the labour market, including what are the skills most of the employers are seeking, what is the level of training and expertise of the candidates, what are the salary expectations of the applicants and so on.The rest of the paper is organized as follows. The next section presents a literature review about the different approaches used to implement a job recommender system. In section 3 we propose an ontology for the IT recruitment domain that helps to automatically match job offers with the candidates' profiles and in reverse, designed to simplify and streamline the recruitment process. Section 4 presents an architecture that uses the defined ontology to implement an e-Recruitment platform, based on Java technologies. In section 5 we illustrate the main features of the implemented prototype of the job recommender system. Finally, section 6 presents the conclusions to our work and the future work remarks, which can improve and extend the presented solution.2Related WorkIn the existing literature [1] [2] there are four main categories used to classify recommender systems:* Collaborative Filtering (CF) - uses the user-to-user correlation method to predict the unknown preferences of a new user based on the preferences of similar users;* Content-Based Filtering (CBF) - recommends items having similar content with the one the user has previously viewed or selected;* Knowledge-Based Approach - makes suggestions based on inferences about the needs and preferences of the user;* Hybrid Approach - combines one or more of the already mentioned methods to obtain better performance.Developing a strong job recommender system is definitely not an easy task. There have always been different problems that needed to be solved. The first encountered problem that matching systems had was the amount of semi-structured data which contained many free text fields remaining unfilled. [3] proposes using Structured Relevance Models (SRM) as a solution for the fields that are missing when a candidate fills an online CV. The authors also emphasize that a blank field value can be inferred based on the context the other fields in the record provide. The SRM is a first example of a Collaborative Filtering approach.Further studies in the e-Recruitment domain suggested developing an automated recommendation system based on supervised machine learning [4], technique that uses the Content-Based Filtering approach. …