Transportation Recommendation with Fairness Consideration

Recent years have witnessed the widespread use of online map services to recommend transportation routes involving multiple transport modes, such as bus, subway, and taxi. However, existing transportation recommendation services mainly focus on improving the overall user click-through rate that is dominated by mainstream user groups, and thus may result in unsatisfactory recommendations for users with diversified travel needs. In other words, different users may receive unequal services. To this end, in this paper, we first identify two types of unfairness in transportation recommendation, (i) the under-estimate unfairness which reflects lower recommendation accuracy (i.e., the quality), and (ii) the under-recommend unfairness which indicates lower recommendation volume (i.e., the quantity) for users who travel in certain regions and during certain time periods. Then, we propose the Fairness-Aware Spatiotemporal Transportation Recommendation (FASTR) framework to mitigate the transportation recommendation bias. In particular, based on a multi-task wide and deep learning model, we propose the dual-focal mechanism for under-estimate mitigation and tailor-designed spatiotemporal fairness metrics and regularizers for under-recommend mitigation. Finally, extensive experiments on two real-world datasets verify the effectiveness of our approach to handle these two types of unfairness.