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Computational Economics for Data Analytics

With the continuous shifting of human activities from offline to online, the Web is no longer just a platform for information sharing and transmission, but a huge online economy where various products or services are distributed from producers to consumers. As a result, 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 or search. Economic Recommendation aims at divising recommendation and resource allocation algorithms for the Web economy based on principled economic theories or intuitions, so as to achieve targeted goals in online resource allocation, such as intelligent marketing, welfare distribution between consumers and producers, improving economic efficiency, cost reduction in terms of price, time, location, etc.

Related Publications:

[1] Yongfeng Zhang, Qi Zhao, Yi Zhang, Daniel Friedman, Min Zhang, Yiqun Liu, and Shaoping Ma. Economic Recommendation with Surplus Maximization. In Proceedings of the 25th International World Wide Web Conference (WWW 2016), April 11 - 15, 2016, Montreal, Canada.
[2] Qi Zhao, Yongfeng Zhang, Yi Zhang, and Daniel Friedman. Multi-Product Utility Maximization for Economic Recommendation. In Proceedings of the 10th International Conference on Web Search and Data Mining (WSDM 2017), February 6 - 10, 2017, Cambridge, UK.
[3] Yongfeng Zhang, Yi Zhang and Daniel Friedman. Economic Recommendation based on Pareto Efficient Resource Allocation. Science Center Berlin for Social Research Discussion Papers, Wissenschaftszentrum Berlin für Sozialforschung.
[4] Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, Shaoping Ma. Fairness-Aware Group Recommendation with Pareto Efficiency. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys 2017), August 27 - 31, 2017, Como, Italy.
[5] Xiao Lin, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. Boosting Moving Average Reversion Strategy for Online Portfolio Selection: A Meta-Learning Approach. In Proceedings of the 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017), March 27 - 30, 2017, Suzhou, China.