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Computational pricing in Internet era

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Abstract

Pricing plays a central rule to a company’s profitability, and therefore has been extensively studied in the literature of economics. When designing a pricing mechanism/ model, an important principle to consider is “price discrimination”, which refers to selling the same resources with different prices according to different values of buyers. 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 usually needed to determine the optimal allocation of sellers’ resources (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 realtime 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 computational 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 of large scale data. Moreover, we make discussions on future research directions on computational pricing, which hopefully can inspire more researchers to contribute to this important domain.

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  1. Microsoft Research Asia, Beijing, 100080, China

    Fei Tian, Tao Qin & Tie-Yan Liu

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  1. Fei Tian
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Fei Tian is an associate researcher in Machine Learning Group, Microsoft Research Asia (MSRA), China. He obtained his PhD degree majoring in computer science in July, 2016, from a joint PhD program between University of Science and Technology of China and MSRA. His research interests mainly lie in machine learning, especially deep learning (with its application in text understanding), and reinforcement learning.

Tao Qin is a lead researcher in Microsoft Research Asia, China. His research interests include machine learning (with the focus on deep learning and reinforcement learning), artificial intelligence (with applications to board games and finance), game theory (with applications to cloud computing, online and mobile advertising, ecommerce), information retrieval and computational advertising. He has served multiple top conferences as area chairs, (senior) PC members, including SIGIR, AAMAS, ACML, NIPS, ICML, IJCAI, AAAI, KDD, WWW, WSDM, CIKM, EC. He got his PhD degree and Bachelor degree both from Tsinghua University, China. He is a member of ACM and IEEE, and an adjunct professor (PhD advisor) in the University of Science and Technology of China.

Tie-Yan Liu is a principal researcher of Microsoft Research Asia, China, and an adjunct/honorary professor at Carnegie Mellon University (CMU), USA, University of Nottingham, UK, and several other universities in China. He has been the general chair, program committee chair, local chair, or area chair for a dozen of top conferences including SIGIR, WWW, KDD, ICML, NIPS, IJCAI, AAAI, ICTIR, as well as associate editor/editorial board member of ACM Transactions on Information Systems, ACM Transactions on the Web, Neurocomputing, Information Retrieval Journal, and Foundations and Trends in Information Retrieval. He is a fellow of the IEEE, a distinguished member of the ACM, an academic committee member of the CCF, and a vice chair of the CIPS information retrieval technical committee.

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Tian, F., Qin, T. & Liu, TY. Computational pricing in Internet era. Front. Comput. Sci. 12, 40–54 (2018). https://doi.org/10.1007/s11704-017-6005-0

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  • DOI: https://doi.org/10.1007/s11704-017-6005-0

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