Abstract
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.
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References
Beutel, A., et al.: Fairness in recommendation ranking through pairwise comparisons. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2212–2220 (2019)
Beutel, A., Chi, E.H., Cheng, Z., Pham, H., Anderson, J.: Beyond globally optimal: focused learning for improved recommendations. In: Proceedings of the 26th International Conference on World Wide Web, pp. 203–212 (2017)
Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: amortizing individual fairness in rankings. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 405–414 (2018)
Calders, T., Verwer, S.: Three Naive Bayes approaches for discrimination-free classification. Data Min. Knowl. Disc. 21(2), 277–292 (2010). https://doi.org/10.1007/s10618-010-0190-x
Chaney, A.J.B., Stewart, B.M., Engelhardt, B.E.: How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In: Proceedings of the 12th ACM Conference on Recommender Systems - RecSys 2018 (2018)
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y.: Xgboost: extreme gradient boosting. R package version 0.4-2, pp. 1–4 (2015)
Cheng, H.T., Koc, L., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender systems (2016)
Crowson, C.S., Atkinson, E.J., Therneau, T.M.: Assessing calibration of prognostic risk scores. Stat. Methods Med. Res. 25(4), 1692–1706 (2016)
Das, A., Dantcheva, A., Bremond, F.: Mitigating bias in gender, age and ethnicity classification: a multi-task convolution neural network approach. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 573–585. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11009-3_35
Fu, L., Sun, D., Rilett, L.R.: Heuristic shortest path algorithms for transportation applications: state of the art. Comput. Oper. Res. 33, 3324–3343 (2006)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017)
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems, pp. 3315–3323 (2016)
Liu, H., Han, J., Fu, Y., Zhou, J., Lu, X., Xiong, H.: Multi-modal transportation recommendation with unified route representation learning. Proc. VLDB Endow. 14(3), 342–350 (2021)
Liu, H., Tong, Y., Han, J., Zhang, P., Lu, X., Xiong, H.: Incorporating multi-source urban data for personalized and context-aware multi-modal transportation recommendation. IEEE Trans. Knowl. Data Eng. (2020)
Liu, H., Tong, Y., Zhang, P., Lu, X., Duan, J., Xiong, H.: Hydra: a personalized and context-aware multi-modal transportation recommendation system. In: Proceedings of the 25th ACM SIGKDD (2019)
Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)
Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD (2018)
Wang, Z., She, Q., Ward, T.E.: Generative adversarial networks in computer vision: a survey and taxonomy. arXiv preprint arXiv:1906.01529 (2019)
Xu, T., Zhu, H., Xiong, H., Zhong, H., Chen, E.: Exploring the social learning of taxi drivers in latent vehicle-to-vehicle networks. IEEE TMC 19, 1804–1817 (2019)
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 1–41 (2015)
Zhong, H., et al.: Adam revisited: a weighted past gradients perspective. Front. Comput. Sci. 14(5), 1–16 (2020). https://doi.org/10.1007/s11704-019-8457-x
Acknowledgement
This research was partially supported by grants from the National Key Research and Development Program of China (Grant No. 2018YFB1402600), and the National Natural Science Foundation of China (Grant No. 91746301, 62072423). And the work was done when the first author interned in Baidu Research.
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Zhou, D., Liu, H., Xu, T., Zhang, L., Zha, R., Xiong, H. (2021). Transportation Recommendation with Fairness Consideration. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_40
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