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Maximizing Gain over Flexible Attributes in Peer to Peer Marketplaces

  • Abolfazl AsudehEmail author
  • Azade Nazi
  • Nick Koudas
  • Gautam Das
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

Peer to peer marketplaces enable transactional exchange of services directly between people. In such platforms, those providing a service are faced with various choices. For example in travel peer to peer marketplaces, although some amenities (attributes) in a property are fixed, others are relatively flexible and can be provided without significant effort. Providing an attribute is usually associated with a cost. Naturally, different sets of attributes may have a different “gains” for a service provider. Consequently, given a limited budget, deciding which attributes to offer is challenging.

In this paper, we formally introduce and define the problem of Gain Maximization over Flexible Attributes (GMFA) and study its complexity. We provide a practically efficient exact algorithm to the GMFA problem that can handle any monotonic gain function. Since the users of the peer to peer marketplaces may not have access to any extra information other than existing tuples in the database, as the next part of our contribution, we introduce the notion of frequent-item based count (FBC), which utilizes nothing but the database itself. We conduct a comprehensive experimental evaluation on real data from AirBnB and a case study that confirm the efficiency and practicality of our proposal.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MichiganAnn ArborUSA
  2. 2.Google AIMountain ViewUSA
  3. 3.University of TorontoTorontoCanada
  4. 4.University of Texas at ArlingtonArlingtonUSA

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