Computational pricing in Internet era  

作　　者：Fei TIAN Tao QIN Tie-Yan LIU 

机构地区：[1]Microsoft Research Asia, Beijing 100080, China

出　　处：《Frontiers of Computer Science》2018年第1期40-54,共15页中国计算机科学前沿（英文版）

摘　　要：Pricing plays a central rule to a company＇s prof- itability, and therefore has been extensively studied in the literature of economics. When designing a pricing mech- anism/model, an important principle to consider is ＂price discrimination＂, which refers to selling the same resources with different prices according to different values of buy- ers. To meet the ＂price discrimination＂ principle, especially when the number of buyers is large, computational methods, which act in a more accurate and principled way, are usu- ally needed to determine the optimal allocation of sellers＇ re- sources （whom to sell to） and the optimal payment of buyers （what to charge）. Nowadays, in the Internet era in which quite a lot of buy and sell processes are conducted through Internet, the design of computational pricing models faces both new challenges and opportunities, considering that （i） nearly real- time interactions between people enable the buyers to reveal their needs and enable the sellers to expose their information in a more expressive manner, （ii） the large-scale interaction data require powerful methods for more efficient processing and enable the sellers to model different buyers in a more precise manner. In this paper, we review recent advances on the analysis and design of computational pricing models for representative Internet industries, e.g., online advertising and cloud computing. In particular, we introduce how computa- tional approaches can be used to analyze buyer＇s behaviors （i.e., equilibrium analysis）, improve resource utilization （i.e., social welfare analysis）, and boost seller＇s profit （i.e., revenue analysis）. We also discuss how machine learning techniques can be used to better understand buyer＇s behaviors and design more effective pricing mechanisms, given the availability oflarge scale data. Moreover, we make discussions on future re- search directions on computational pricing, which hopefully can inspire more researchers to contribute to this important dPricing plays a central rule to a company＇s prof- itability, and therefore has been extensively studied in the literature of economics. When designing a pricing mech- anism/model, an important principle to consider is ＂price discrimination＂, which refers to selling the same resources with different prices according to different values of buy- ers. To meet the ＂price discrimination＂ principle, especially when the number of buyers is large, computational methods, which act in a more accurate and principled way, are usu- ally needed to determine the optimal allocation of sellers＇ re- sources （whom to sell to） and the optimal payment of buyers （what to charge）. Nowadays, in the Internet era in which quite a lot of buy and sell processes are conducted through Internet, the design of computational pricing models faces both new challenges and opportunities, considering that （i） nearly real- time interactions between people enable the buyers to reveal their needs and enable the sellers to expose their information in a more expressive manner, （ii） the large-scale interaction data require powerful methods for more efficient processing and enable the sellers to model different buyers in a more precise manner. In this paper, we review recent advances on the analysis and design of computational pricing models for representative Internet industries, e.g., online advertising and cloud computing. In particular, we introduce how computa- tional approaches can be used to analyze buyer＇s behaviors （i.e., equilibrium analysis）, improve resource utilization （i.e., social welfare analysis）, and boost seller＇s profit （i.e., revenue analysis）. We also discuss how machine learning techniques can be used to better understand buyer＇s behaviors and design more effective pricing mechanisms, given the availability oflarge scale data. Moreover, we make discussions on future re- search directions on computational pricing, which hopefully can inspire more researchers to contribute to this important d

关 键 词：computational pricing price discrimination on-line advertising cloud computing mechanism design 

分 类 号：S181[农业科学—农业基础科学] TP393.4[自动化与计算机技术—计算机应用技术]