ReCon: Reducing Congestion in Job Recommendation using Optimal Transport

TL;DR

ReCon is a novel job recommendation method that uses optimal transport to reduce congestion by evenly distributing vacancies among job seekers, improving efficiency and user satisfaction.

Contribution

It introduces a multi-objective optimization framework combining optimal transport with recommendation models to address congestion in job markets.

Findings

ReCon reduces congestion effectively in real-world datasets.

It maintains high recommendation quality as measured by NDCG.

ReCon outperforms baseline methods in balancing recommendation diversity and relevance.

Abstract

Recommender systems may suffer from congestion, meaning that there is an unequal distribution of the items in how often they are recommended. Some items may be recommended much more than others. Recommenders are increasingly used in domains where items have limited availability, such as the job market, where congestion is especially problematic: Recommending a vacancy -- for which typically only one person will be hired -- to a large number of job seekers may lead to frustration for job seekers, as they may be applying for jobs where they are not hired. This may also leave vacancies unfilled and result in job market inefficiency. We propose a novel 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. We evaluated our approach on two real-world job market datasets. The evaluation results show that ReCon has good performance on both congestion-related (e.g., Congestion) and desirability (e.g., NDCG) measures.