  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.
