Maximizing Gain over Flexible Attributes in Peer to Peer Marketplaces
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.
Abolfazl Asudeh Azade Nazi Nick Koudas Gautam Das
University of Michigan,Ann Arbor,USA Google AI,Mountain View,USA University of Toronto,Toronto,Canada University of Texas at Arlington,Arlington,USA
国际会议
澳门
英文
327-345
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）