A dynamic, constantly shifting labor market creates enormous job postings, overwhelming candidates and making it difficult for businesses to find quality candidates. It is also hard for job seekers to find suitable jobs. Addressing these issues, machine learning-driven job recommender systems have recently become an essential tool using predictive models to improve the match between jobs and candidates. A hybrid design that combines collaborative filtering with content-based filtering and adds contextual information like geographic location, industry trends, and user behavioural data can enhance the accuracy and relevance of recommendations. This paper reviews and critically analyzes contemporary job recommender system techniques. The focus is on hybrid recommendation models and the integration of algorithmic approaches, indicating their strengths and weaknesses. This review also looks into the evaluation metrics like precision, recall, normalized discounted cumulative gain (NDCG), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). To provide an overall perspective of the various approaches employed and the performance trade-offs inherent therein, this paper hopes to shed some light on the optimization of job recommendation systems for better effectiveness and user satisfaction.
Yap et al. (Wed,) studied this question.