User interaction analysis to recommend suitable jobs in career-oriented social networking sites

Career-oriented social networking sites are very much useful for job seekers to find a suitable job and useful for recruiters as well to find the right candidate for a job. Job recommendation system helps job seekers to find appropriate jobs matching with their profile. So, it can be considered as recruiters approaching a suitable candidate whenever they have an appropriate job for them. In this paper, we present a research technique of developing a job recommendation system for the online job hunting websites to predict suitable job postings that are likely to be relevant to the user i.e., the job postings with which the users can possibly interact. Relevant jobs are those job postings on which a user may click, bookmark or reply to the recruiter. Here, we have considered all possible factors related to users as well as job items available in a publicly available partial big data set of a widely used international job hunting website. We have split the interaction data into training and test data for the purpose of evaluating our proposed system. After that we have developed an algorithm for job recommender system which can calculate similarity for user-user, item-item and hybrid of user-user and item-item perspective between training and test data set based on weighted scores using our proposed similarity computation algorithm for different jobs and users all the information present in the dataset. We have used Collaborative Filtering (CF) algorithm separately for user-user and item-item based approach and for hybrid approach, we have calculated the intersection between user-user and item-item based recommended list and select top-k job items as recommended lists from the intersection. After that, we have compared the predicted recommended list based on all three approaches with the actual list and made offline evaluation of the job recommender system accuracy based on obtained score. Finally we have found that hybrid approach performs better than user-user and item-item based approach for entire 90% sparsity of the training data.

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