Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/142710
Authors: 
Zhang, Yongfeng
Zhao, Qi
Zhang, Yi
Friedman, Daniel
Zhang, Min
Liu, Yiqun
Ma, Shaoping
Year of Publication: 
2016
Series/Report no.: 
WZB Discussion Paper SP II 2016-502
Abstract: 
A prime function of many major World Wide Web applications is Online Service Allocation (OSA), the function of matching individual consumers with particular services/goods (which may include loans or jobs as well as products) each with its own producer. In the applications of interest, consumers are free to choose, so OSA usually takes the form of personalized recommendation or search in practice. The performance metrics of recommender and search systems currently tend to focus on just one side of the match, in some cases the consumers (e.g. satisfaction) and in other cases the producers (e.g., profit). However, a sustainable OSA platform needs benefit both consumers and producers; otherwise the neglected party eventually may stop using it. In this paper, we show how to adapt economists' traditional idea of maximizing total surplus (the sum of consumer net benefit and producer profit) to the heterogeneous world of online service allocation, in an effort to promote the web intelligence for social good in online eco-systems. Modifications of traditional personalized recommendation algorithms enable us to apply Total Surplus Maximization (TSM) to three very different types of real-world tasks - e-commerce, P2P lending and freelancing. The results for all three tasks suggest that TSM compares very favorably to currently popular approaches, to the benefit of both producers and consumers.
Subjects: 
Total Surplus Maximization
Online Service Allocation
Computational Economics
Recommendation Systems
Web-based Services
Document Type: 
Working Paper

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