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.

翻译：推荐系统可能面临拥塞问题，即不同项目被推荐的频率存在不均衡分布，某些项目被推荐的次数远高于其他项目。随着推荐系统越来越多地应用于项目可用性有限的领域（如就业市场），拥塞问题尤为突出：将某个空缺职位（通常仅有一人能被录用）推荐给大量求职者，可能导致求职者因申请了无法被录用的职位而产生挫败感，同时也会造成职位空缺无法填补，进而降低就业市场效率。我们提出了一种名为ReCon的新型职位推荐方法，专门解决拥塞问题。该方法通过引入最优传输组件，确保空缺职位在求职者之间更均衡地分布，并将职位推荐模型整合为一个多目标优化问题。我们在两个真实就业市场数据集上评估了该方法，结果表明，ReCon在拥塞相关指标（如拥塞度）和推荐质量指标（如NDCG）上均表现出优异性能。