在对等市场中协助服务提供商：最大化灵活属性的收益
Assisting Service Providers In Peer-to-peer Marketplaces: Maximizing Gain Over Flexible Attributes
摘要：
像AirBnB这样的点对点市场可以直接在人与人之间进行服务的交易交换。在这样的平台中，提供服务的人（AirBnB中的主机）面临着各种选择。例如，在AirBnB中，尽管财产中的一些设施（财产的属性）是固定的，但其他设施相对灵活并且可以在没有显著努力的情况下提供。提供便利设施通常与成本有关。当然，不同的设施对主人来说可能有不同的“好处”。因此，在预算有限的情况下，决定提供哪些设施（属性）是具有挑战性的。在本文中，我们正式介绍和定义的问题的增益最大化柔性属性（GMFA）。我们首先证明，这个问题是NP-难的，并表明，确定一个近似算法与一个常数近似比是不太可能的。然后，我们提供了一个实际有效的精确算法的GMFA问题的一般类的单调增益函数，量化的属性集的好处。作为我们的贡献的下一部分，我们专注于一个实用的增益函数的GMFA的设计。我们引入了基于频繁项的计数（FBC）的概念，它利用数据库中现有的元组来定义增益的概念，并提出了一个有效的算法来计算它。我们提出了一个全面的实验评估结果，从AirBnB的真实数据集上提出的技术，并证明了我们的建议的实际意义和实用性。
英文摘要：
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.
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