Assisting Service Providers In Peer-to-peer Marketplaces: Maximizing Gain Over Flexible Attributes

TL;DR

This paper introduces the Gain Maximization over Flexible Attributes (GMFA) problem in peer-to-peer marketplaces, proving its NP-hardness, and offers efficient algorithms for practical gain maximization using real Airbnb data.

Contribution

It formally defines GMFA, proves its NP-hardness, and provides an exact algorithm for monotonic gain functions along with a practical gain function based on frequent-item counts.

Findings

The GMFA problem is NP-hard.

The proposed exact algorithm efficiently solves GMFA for monotonic gain functions.

Experimental results on Airbnb data demonstrate the effectiveness of the algorithms.

Abstract

Peer to peer marketplaces such as AirBnB enable transactional exchange of services directly between people. In such platforms, those providing a service (hosts in AirBnB) are faced with various choices. For example in AirBnB, although some amenities in a property (attributes of the property) are fixed, others are relatively flexible and can be provided without significant effort. Providing an amenity is usually associated with a cost. Naturally different sets of amenities may have a different "gains" for a host. Consequently, given a limited budget, deciding which amenities (attributes) to offer is challenging. In this paper, we formally introduce and define the problem of Gain Maximization over Flexible Attributes (GMFA). We first prove that the problem is NP-hard and show that identifying an approximate algorithm with a constant approximate ratio is unlikely. We then provide a practically efficient exact algorithm to the GMFA problem for the general class of monotonic gain functions, which quantify the benefit of sets of attributes. As the next part of our contribution, we focus on the design of a practical gain function for GMFA. We introduce the notion of frequent-item based count (FBC), which utilizes the existing tuples in the database to define the notion of gain, and propose an efficient algorithm for computing it. We present the results of a comprehensive experimental evaluation of the proposed techniques on real dataset from AirBnB and demonstrate the practical relevance and utility of our proposal.