Scalable Job Recommendation With Lower Congestion Using Optimal Transport

Abstract

Recommender systems often face congestion, characterized by an uneven distribution in the frequency of item recommendations. The presence of congestion in recommendations is especially problematic in domains where users or items have limited availability. For example, recommending one vacancy to many job seekers results in frustration of job seekers and job market inefficiency. We propose a novel in-processing approach to job recommendation called ReCon, accounting for the congestion problem. Our approach is to use an optimal transport component to ensure a more equal spread of vacancies over job seekers, combined with a job recommendation model in a multi-objective optimization problem. Moreover, we propose a scalable solution so that ReCon is applicable to large-scale datasets. We evaluated our approach on several real-world job market datasets. The evaluation results show that ReCon has good performance on both congestion-related (e.g., Congestion, Coverage, and Gini Index) and desirability (e.g., NDCG, Recall, and Hit Rate) measures. In most cases, ReCon is Pareto optimal for some selections of hyper-parameters in comparison to the baselines.

Keywords

• Job recommendation
• optimal transport
• congestion
• exposure fairness
• aggregate diversity