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Economic recommendation based on pareto efficient resource allocation

Author

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  • Zhang, Yongfeng
  • Zhang, Yi
  • Friedman, Daniel

Abstract

A fundamentally important role of the Web economy is Online Resource Allocation (ORA) from producers to consumers, such as product allocation in E-commerce, job allocation in freelancing platforms, and driver resource allocation in P2P riding services. Since users have the freedom to choose, such allocations are not provided in a forced manner, but usually in forms of personalized recommendation, where users have the right to refuse. Current recommendation approaches mostly provide allocations to match the preference of each individual user, instead of treating the Web application as a whole economic system where users therein are mutually correlated on the allocations. This lack of global view leads to Pareto inefficiency, i.e., we can actually improve the recommendations by bettering some users while not hurting the others, and it means that the system did not achieve its best possible allocation. This problem is especially severe when the total amount of each resource is limited, so that its allocation to one (set of) user means that other users are left out. In this paper, we propose Pareto Efficient Economic Recommendation (PEER) - that the system provides the best possible (i.e., Pareto optimal) recommendations, where no user can gain further benefits without hurting the others. To this end, we propose a Multi-Objective Optimization (MOO) framework to maximize the surplus of each user simultaneously, and provide recommendations based on the resulting Pareto optima. To benefit the many existing recommendation algorithms, we further propose a Pareto Improvement Process (PIP) to turn their recommendations into Pareto efficient ones. Experiments on real-world datasets verify that PIP improves existing algorithms on recommendation performance and consumer surplus, while the direct PEER approach gains the best performance on both aspects.

Suggested Citation

  • Zhang, Yongfeng & Zhang, Yi & Friedman, Daniel, 2017. "Economic recommendation based on pareto efficient resource allocation," Discussion Papers, Research Professorship Market Design: Theory and Pragmatics SP II 2017-503, WZB Berlin Social Science Center.
  • Handle: RePEc:zbw:wzbmdn:spii2017503
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    References listed on IDEAS

    as
    1. Daniel Friedman & József Sákovics, 2015. "Tractable consumer choice," Theory and Decision, Springer, vol. 79(2), pages 333-358, September.
    2. Kenneth Arrow, 1962. "Economic Welfare and the Allocation of Resources for Invention," NBER Chapters, in: The Rate and Direction of Inventive Activity: Economic and Social Factors, pages 609-626, National Bureau of Economic Research, Inc.
    3. Zhang, Yongfeng & Zhao, Qi & Zhang, Yi & Friedman, Daniel & Zhang, Min & Liu, Yiqun & Ma, Shaoping, 2016. "Economic recommendation with surplus maximization," Discussion Papers, Research Professorship Market Design: Theory and Pragmatics SP II 2016-502, WZB Berlin Social Science Center.
    4. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    5. Richard E. Just & Darell L. Hueth & Andrew Schmitz (ed.), 2008. "Applied Welfare Economics," Books, Edward Elgar Publishing, number 12892.
    6. Bogdan Tomoiagă & Mircea Chindriş & Andreas Sumper & Antoni Sudria-Andreu & Roberto Villafafila-Robles, 2013. "Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II," Energies, MDPI, vol. 6(3), pages 1-17, March.
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    More about this item

    Keywords

    Pareto Efficiency; Online Resource Allocation; Multi-Objective Optimization; Economic Recommendation; Computational Economics;
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