Getahun, Fekade (PhD)Abebe, Selam2020-09-142023-11-092020-09-142023-11-092020-06-05http://10.90.10.223:4000/handle/123456789/22319Companies often receive thousands of resumes for each job posting and employ dedicated screeners to short list quailed applicants. Searching for jobs online is an information intensive activity, because thousands of jobs are posted on the Web daily and it takes a great deal of effort to find the right position. Job search sites require recommender systems to meet diversified information needs. In this thesis work, we introduce a context aware job recommender which not only produces recommendation based on resume and job description, it also had integrated the preferences of the job seeker to enhance the recommendation. From the user’s perspective, three different kinds of recommenders are implemented collaborative filtering based, content based and context/ preference based. Users of this system can retrieve jobs with different methods. From the recruiters’ perspective, two different kinds of recommenders are implemented content based and context/ preference based. Recruiter can retrieve candidate job seekers based on their resumes or likelihood of the job seeker with the job based on the job seekers preferences. A challenge lies on the design of recommendation approaches since different job seekers might have diverse features and interests. To address the above-mentioned problem we integrate context/preferences of a user with their respective profile. In our evaluation we show that personalized recommendation can be enhanced by integrating contextual information to a user profile.enRecommendation SystemJob RecommendationPersonalized RecommendationThesis