Economic recommendation with surplus maximization

Yongfeng Zhang, Qi Zhao, Yi Zhang, Daniel Friedman, Min Zhang, Yiqun Liu, Shaoping Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations


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.

Original languageEnglish (US)
Title of host publication25th International World Wide Web Conference, WWW 2016
PublisherInternational World Wide Web Conferences Steering Committee
Number of pages11
ISBN (Electronic)9781450341431
StatePublished - 2016
Externally publishedYes
Event25th International World Wide Web Conference, WWW 2016 - Montreal, Canada
Duration: Apr 11 2016Apr 15 2016

Publication series

Name25th International World Wide Web Conference, WWW 2016


Other25th International World Wide Web Conference, WWW 2016

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software


  • Computational Economics
  • Online Service Allocation
  • Recommendation Systems
  • Total SurplusMaximization
  • Web-based Services


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