Amanulla Ashraff
The increasing importance of the web as a medium for electronic and business transactions has served as a catalyst or rather a driving force for the introduction and implementation of recommender systems. Recommender Systems plays a major role in processing and analyzing thousands of data rows or reviews and help humans make a purchase decision of a product or service. It also has the ability to predict whether a particular user would rate a product or service based on the user’s profile behavioral pattern. At present, Recommender Systems are being used extensively in every domain known to us. They are said to be ubiquitous. However, in the field of recruitment it’s not being utilized exclusively.
A recent statistics shows an increase in staff turnover which has negatively impacted the organization as well as the employee. The reasons being company culture, working flexibility (work from home opportunity), no learning advancements and pay scale. Further investigations revealed that there are lacking guidance or support which helps a job seeker find the company that will suit him best and though there’s information available about companies, job seekers can’t read all the reviews by themselves and get an analytical decision.
Most research up to this point has focused on recommending jobs accurately based on collaborative filtering, knowledge filtering and hybrid approaches but no research has been done in this domain where companies are suggested to job seekers and no such combinational approach has been used elsewhere.
In this paper, a new approach has been proposed to study the available review data on IT companies (score their reviews based on user review sentiments) and collect information on job seekers including their Psychometric evaluations and presents the job seeker with useful information or rather outputs on which company is most suitable for the job seeker.
The theoretical approach, Algorithmic approach and the importance of a system like this will be discussed in this paper.
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