Hybrid immunizing solution for job recommender system

Abstract

Two traditional recommendation techniques, content-based and collaborative filtering (CF), have been widely used in a broad range of domain areas. Both methods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommendation problem from applicant’s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system (AIS), which has a powerful exploration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors’ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.

Keywords

content-based / collaborative filtering (CF) / hybridization / computational intelligence (CI) / artificial immune system (AIS) / clonal selection / correlation-based similarity

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