Abstract
Instant delivery platforms, equipped with professional couriers to provide convenient delivery services, have emerged rapidly in many cities. For the benefit of platforms, many researchers focus more on maximizing overall efficiency but ignore individual fairness. Current fairness research in mobile systems mainly concentrates on one-sided or two-sided relationships, such as drivers and customers. However, instant delivery services have two new characteristics in fairness: (i) multi-stakeholder involvement, namely couriers, merchants and users should be considered comprehensively; (ii) more complicated matching relationship because of the concurrent dispatch mode, meaning one courier will handle multiple orders simultaneously. To handle this multi-sided fairness problem, our paper proposes a novel order dispatch system to balance the platform revenue and multi-stakeholder fairness. Motivated by the analysis of real-world datasets, we formulate the order dispatch problem as a sequential decision-making problem and incorporate multi-sided fairness into the decision criteria. Then, we design a multi-sided fairness-aware deep reinforcement learning algorithm to solve large-scale decision problem, with the fairness relying on Least Misery Fairness definition for users and Variance Fairness definition for couriers and merchants. Finally, extensive experiments show the effectiveness of our model in balancing multi-sided fairness among stakeholders and long-term profits of the whole platform.
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Abdollahpouri, H., Burke, R.: Multi-stakeholder recommendation and its connection to multi-sided fairness. CoRR abs/1907.13158 (2019)
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, SIGIR 2018, pp. 405–414. Association for Computing Machinery, New York (2018)
Chakraborty, A., Patro, G.K., Ganguly, N., Gummadi, K.P., Loiseau, P.: Equality of voice: towards fair representation in crowdsourced top-k recommendations, FAT* 2019, pp. 129–138. Association for Computing Machinery, New York (2019)
Ding, Y., et al.: A city-wide crowdsourcing delivery system with reinforcement learning. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5(3), 1–22 (2021)
Guo, B., et al.: Concurrent order dispatch for instant delivery with time-constrained actor-critic reinforcement learning. In: 2021 IEEE Real-Time Systems Symposium (RTSS), pp. 176–187 (2021)
Lei, H., Zhao, Y., Cai, L.: Multi-objective optimization for guaranteed delivery in video service platform. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2020, pp. 3017–3025 (2020)
Li, M., et al.: Efficient ridesharing order dispatching with mean field multi-agent reinforcement learning. In: The World Wide Web Conference, WWW 2019, pp. 983–994 (2019)
Li, Y., Zheng, Y., Yang, Q.: Efficient and effective express via contextual cooperative reinforcement learning. In: KDD 2019 (2019)
Li, Y., Chen, H., Fu, Z., Ge, Y., Zhang, Y.: User-oriented fairness in recommendation. In: Proceedings of the Web Conference 2021, WWW 2021, pp. 624–632 (2021)
Lin, K., Zhao, R., Xu, Z., Zhou, J.: Efficient large-scale fleet management via multi-agent deep reinforcement learning. In: KDD 2018 (2018)
McCann, J., Chatley, R.: Fleet management in on-demand transportation networks: using a greedy approach (2018)
Patro, G.K., Biswas, A., Ganguly, N., Gummadi, K.P., Chakraborty, A.: Fairrec: two-sided fairness for personalized recommendations in two-sided platforms. In: Proceedings of the Web Conference 2020, WWW 2020, pp. 1194–1204 (2020)
Singh, A., Joachims, T.: Fairness of exposure in rankings. In: KDD 2018 (2018)
Sühr, T., Biega, A.J., Zehlike, M., Gummadi, K.P., Chakraborty, A.: Two-sided fairness for repeated matchings in two-sided markets: a case study of a ride-hailing platform. In: The 25th ACM SIGKDD International Conference, KDD 2019 (2019)
Wang, G., Zhang, Y., Fang, Z., Wang, S., Zhang, F., Zhang, D.: Faircharge: a data-driven fairness-aware charging recommendation system for large-scale electric taxi fleets. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4(1), 1–25 (2020)
Wang, G., Zhong, S., Wang, S., Miao, F., Dong, Z., Zhang, D.: Data-driven fairness-aware vehicle displacement for large-scale electric taxi fleets. In: ICDE 2021 (2021)
Wang, Z., Qin, Z., Tang, X., Ye, J., Zhu, H.: Deep reinforcement learning with knowledge transfer for online rides order dispatching. In: ICDM 2018 (2018)
Xiao, L., Min, Z., Yongfeng, Z., Zhaoquan, G., Yiqun, L., Shaoping, M.: Fairness-aware group recommendation with pareto-efficiency. In: RecSys 2017 (2017)
Xu, Z., et al.: Large-scale order dispatch in on-demand ride-hailing platforms: a learning and planning approach. In: KDD 2018 (2018)
Zhang, L., et al.: A taxi order dispatch model based on combinatorial optimization. In: The 23rd ACM SIGKDD International Conference, KDD 2017, pp. 2151–2159 (2017)
Zhang, Y., et al.: Route prediction for instant delivery. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(3), 1–25 (2019)
Zhou, M., et al.: Multi-agent reinforcement learning for order-dispatching via order-vehicle distribution matching. In: CIKM 2019 (2019)
Acknowledgements
The authors would like to thank the anonymous reviewers for their constructive and helpful feedback. This work was supported in part by Science and Technology Innovation 2030 - Major Project 2021ZD0114202.
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Cao, Z., Jiang, L., Zhou, X., Zhu, S., Wang, H., Wang, S. (2022). Toward Multi-sided Fairness: A Fairness-Aware Order Dispatch System for Instant Delivery Service. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_25
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DOI: https://doi.org/10.1007/978-3-031-19214-2_25
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