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.
A. Asudeh and A. Nazi—Work done at the University of Texas at Arlington.
This work was supported in part by NSF grant No. 1745925, grant W911NF-15-1-0020 from the Army Research Office, and a grant from AT&T.
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Notes
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Monotonicity of the gain function simply means that adding a new attribute does not reduce the gain.
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Depending on the application it may represent a monetary value.
- 6.
In addition to the input set of attributes, the function gain(.) may depend to other variables such as the number of attribute (n); one such function is discussed in Sect. 4.
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Please note that the GMFA is NP-complete even for the polynomial time gain functions.
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For simplicity, we use \(Q(v,\mathcal {D})\) to refer to \(Q(\mathcal {A}(v),\mathcal {D})\).
- 10.
Note that if \(P[k]=X\), \(A_k\) may or may not belong to \(\mathcal {A}_i\).
References
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: VLDB (1994)
Albers, S., Brockhoff, K.: Optimal product attributes in single choice models. JORS 37, 647–655 (1980)
Asudeh, A., Zhang, N., Das, G.: Query reranking as a service. PVLDB 9(11), 888–899 (2016)
Bertsekas, D.P., Özveren, C., Stamoulis, G.D., Tseng, P., Tsitsiklis, J.N.: Optimal communication algorithms for hypercubes. JPDC 11(4), 263–275 (1991)
Das, M., Das, G., Hristidis, V.: Leveraging collaborative tagging for web item design. In: SIGKDD (2011)
Ertz, M., Durif, F.: Collaborative consumption or the rise of the two-sided consumer. J. Bus. Manage. 4, 195–209 (2016)
Feldman, M., Izsak, R.: Constrained monotone function maximization and the supermodular degree. arXiv preprint arXiv:1407.6328 (2014)
Gary, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, New York (1979)
Geerts, F., Goethals, B., Bussche, J.: Tight upper bounds on the number of candidate patterns. TODS 30, 333–363 (2005)
Gunopulos, D., Khardon, R., Mannila, H., Saluja, S., Toivonen, H., Sharma, R.S.: Discovering all most specific sentences. TODS 28, 140–174 (2013)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD. ACM (2000)
Lhote, L., Rioult, F., Soulet, A.: Average number of frequent (closed) patterns in Bernoulli and Markovian databases. In: ICDM. IEEE (2005)
Miah, M., Das, G., Hristidis, V., Mannila, H.: Determining attributes to maximize visibility of objects. TKDE 21, 959–973 (2009)
Rader Jr., D.J., Woeginger, G.J.: The quadratic 0–1 knapsack problem with series-parallel support. Oper. Res. Lett. 30(3), 159–166 (2002)
Asudeh, A., Nazi, A., Koudas, N., Das, G.: Assisting service providers in peer-to-peer marketplaces: maximizing gain over flexible attributes. CoRR, abs/1705.03028 (2017)
Rymon, R.: Search through systematic set enumeration (1992)
Selker, T., Burleson, W.: Context-aware design and interaction in computer systems. IBM Syst. J. 39, 880–891 (2000)
Shocker, A.D., Srinivasan, V.: A consumer-based methodology for the identification of new product ideas. Manage. Sci. 20, 921–937 (1974)
Witzgall, C.: Mathematical methods of site selection for electronic message systems (EMS). NASA STI/Recon Tech, Report (1975)
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Asudeh, A., Nazi, A., Koudas, N., Das, G. (2019). Maximizing Gain over Flexible Attributes in Peer to Peer Marketplaces. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_26
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