Yongxin Tong
Lei Chen
ABSTRACT
In spatial crowdsourcing, requesters submit their task-related locations and increase the demand of a local area. The platform prices these tasks and assigns spatial workers to serve if the prices are accepted by requesters. There exist mature pricing strategies which specialize in tackling the imbalance between supply and demand in a local market. However, in global optimization, the platform should consider the mobility of workers; that is, any single worker can be the potential supply for several areas, while it can only be the true supply of one area when assigned by the platform. The hardness lies in the uncertainty of the true supply of each area, hence the existing pricing strategies do not work. In the paper, we formally define this <u>G</u>lobal <u>D</u>ynamic <u>P</u>ricing(GDP) problem in spatial crowdsourcing. And since the objective is concerned with how the platform matches the supply to areas, we let the matching algorithm guide us how to price. We propose a <u>MA</u>tching-based <u>P</u>ricing <u>S</u>trategy (MAPS) with guaranteed bound. Extensive experiments conducted on the synthetic and real datasets demonstrate the effectiveness of MAPS.
- Mohammad Asghari, Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, and Yaguang Li. 2016. Price-aware Real-time Ride-sharing at Scale: An Auction-based Approach GIS 2016. 3:1--3:10. Google Scholar
Digital Library
- Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. 2002. Finite-time Analysis of the Multiarmed Bandit Problem. Machine Learning, Vol. 47, 2--3 (2002), 235--256. Google ScholarDigital Library
- Moshe Babaioff, Shaddin Dughmi, Robert D. Kleinberg, and Aleksandrs Slivkins. 2011 b. Dynamic Pricing with Limited Supply. ACM Transactions on Economics and Computation, Vol. 3, 1 (2011), 4:1--4:26. Google ScholarDigital Library
- Moshe Babaioff, Shaddin Dughmi, and Alex Slivkins. 2011 a. Detail-free, Posted-Price Mechanisms for Limited Supply Online Auctions Workshop on Bayesian Mechanism Design 2011.Google Scholar
- Richard E. Barlow, Albert W. Marshall, and Frank Proschan. 1963. Properties of Probability Distributions with Monotone Hazard Rate. The Annals of Mathematical Statistics (1963), 375--389.Google Scholar
- Omar Besbes and Assaf J. Zeevi. 2009. Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms. Operations Research, Vol. 57, 6 (2009), 1407--1420. Google ScholarDigital Library
- Avrim Blum, Vijay Kumar, Atri Rudra, and Felix Wu. 2003. Online Learning in Online Auctions. In SODA 2003. 202--204. Google ScholarDigital Library
- Gruia Cualinescu, Chandra Chekuri, Martin Pál, and Jan Vondrák. 2007. Maximizing a Submodular Set Function Subject to a Matroid Constraint IPCO 2007. 182--196. Google ScholarDigital Library
- Lei Chen and Cyrus Shahabi. 2016. Spatial Crowdsourcing: Challenges and Opportunities. IEEE Data Engineering Bulletin Vol. 39, 4 (2016), 14--25.Google Scholar
- Zhao Chen, Rui Fu, Ziyuan Zhao, Zheng Liu, Leihao Xia, Lei Chen, Peng Cheng, Caleb Chen Cao, Yongxin Tong, and Chen Jason Zhang. 2014. gMission: A General Spatial Crowdsourcing Platform. PVLDB, Vol. 7, 13 (2014), 1629--1632. Google ScholarDigital Library
- Anand Inasu Chittilappilly, Lei Chen, and Sihem Amer-Yahia. 2016. A Survey of General-Purpose Crowdsourcing Techniques. IEEE Transactions on Knowledge and Data Engineering, Vol. 28, 9 (2016), 2246--2266. Google ScholarDigital Library
- Fan R. K. Chung and Lincoln Lu. 2006. Survey: Concentration Inequalities and Martingale Inequalities: A Survey. Internet Mathematics, Vol. 3, 1 (2006), 79--127.Google ScholarCross Ref
- Nilesh N. Dalvi and Dan Suciu. 2007. Management of Probabilistic Data: Foundations and Challenges PODS 2007. 1--12. Google ScholarDigital Library
- Dingxiong Deng, Cyrus Shahabi, and Ugur Demiryurek. 2013. Maximizing the Number of Worker's Self-selected Tasks in Spatial Crowdsourcing GIS 2013. 324--333. Google ScholarDigital Library
- Dingxiong Deng, Cyrus Shahabi, and Linhong Zhu. 2015. Task Matching and Scheduling for Multiple Workers in Spatial Crowdsourcing GIS 2015. 21:1--21:10. Google ScholarDigital Library
- Nikhil R. Devanur and Jason D. Hartline. 2009. Limited and Online Supply and the Bayesian Foundations of Prior-free Mechanism Design EC 2009. 41--50. Google ScholarDigital Library
- Nikhil R. Devanur, Christos H. Papadimitriou, Amin Saberi, and Vijay V. Vazirani. 2008. Market Equilibrium via a Primal-dual Algorithm for a Convex ProgramJ. ACM Vol. 55, 5 (2008), 22:1--22:18. Google ScholarDigital Library
- Eugene F. Fama. 1998. Market Efficiency, Long-term Returns, and Behavioral Finance1. Journal of Financial Economics Vol. 49, 3 (1998), 283--306.Google ScholarCross Ref
- Hector Garcia-Molina, Manas Joglekar, Adam Marcus, Aditya G. Parameswaran, and Vasilis Verroios. 2016. Challenges in Data Crowdsourcing. IEEE Transactions on Knowledge and Data Engineering, Vol. 28, 4 (2016), 901--911. Google ScholarDigital Library
- Shawn R. Jeffery, Minos N. Garofalakis, and Michael J. Franklin. 2006. Adaptive Cleaning for RFID Data Streams. In VLDB 2006. 163--174. Google ScholarDigital Library
- Leyla Kazemi and Cyrus Shahabi. 2012. GeoCrowd: Enabling Query Answering with Spatial Crowdsourcing GIS 2012. 189--198. Google ScholarDigital Library
- Frank Kelly. 1997. Charging and Rate Control for Elastic Traffic. European Transactions on Telecommunications, Vol. 8, 1 (1997), 33--37.Google ScholarCross Ref
- Robert D. Kleinberg and Frank Thomson Leighton. 2003. The Value of Knowing a Demand Curve: Bounds on Regret for Online Posted-Price Auctions FOCS 2003. 594--605. Google ScholarDigital Library
- Guoliang Li, Jiannan Wang, Yudian Zheng, and Michael J. Franklin. 2016. Crowdsourced Data Management: A Survey. IEEE Transactions on Knowledge and Data Engineering, Vol. 28, 9 (2016), 2296--2319. Google ScholarDigital Library
- Guoliang Li, Yudian Zheng, Ju Fan, Jiannan Wang, and Reynold Cheng. 2017. Crowdsourced Data Management: Overview and Challenges SIGMOD 2017. 1711--1716. Google ScholarDigital Library
- JiaXu Liu, Yudian Ji, Weifeng Lv, and Ke Xu. 2017. Budget-Aware Dynamic Incentive Mechanism in Spatial Crowdsourcing. Journal of Computer Science and Technology Vol. 32, 5 (2017), 890--904.Google ScholarCross Ref
- Mohamed Musthag and Deepak Ganesan. 2013. Labor Dynamics in a Mobile Micro-task Market. In CHI 2013. 641--650. Google ScholarDigital Library
- Roger B. Myerson. 1981. Optimal Auction Design. Mathematics of Operations Research Vol. 6, 1 (1981), 58--73. Google ScholarDigital Library
- Paat Rusmevichientong, Benjamin Van Roy, and Peter W. Glynn. 2006. A Nonparametric Approach to Multiproduct Pricing. Operations Research, Vol. 54, 1 (2006), 82--98. Google ScholarDigital Library
- Yaron Singer and Manas Mittal. 2013. Pricing Mechanisms for Crowdsourcing Markets. In WWW 2013. 1157--1166. Google ScholarDigital Library
- Adish Singla and Andreas Krause. 2013. Truthful Incentives in Crowdsourcing Tasks Using Regret Minimization Mechanisms WWW 2013. 1167--1178. Google ScholarDigital Library
- Tianshu Song, Yongxin Tong, Libin Wang, Jieying She, Bin Yao, Lei Chen, and Ke Xu. 2017. Trichromatic Online Matching in Real-Time Spatial Crowdsourcing ICDE 2017. 1009--1020.Google Scholar
- Hien To, Gabriel Ghinita, and Cyrus Shahabi. 2014. A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing. PVLDB, Vol. 7, 10 (2014), 919--930. Google ScholarDigital Library
- Yongxin Tong, Lei Chen, and Cyrus Shahabi. 2017 a. Spatial Crowdsourcing: Challenges, Techniques, and Applications. PVLDB, Vol. 10, 12 (2017), 1988--1991. Google ScholarDigital Library
- Yongxin Tong, Jieying She, Bolin Ding, Lei Chen, Tianyu Wo, and Ke Xu. 2016 a. Online Minimum Matching in Real-Time Spatial Data: Experiments and Analysis. PVLDB, Vol. 9, 12 (2016), 1053--1064. Google ScholarDigital Library
- Yongxin Tong, Jieying She, Bolin Ding, Libin Wang, and Lei Chen. 2016 b. Online Mobile Micro-Task Allocation in Spatial Crowdsourcing ICDE 2016. 49--60.Google Scholar
- Yongxin Tong, Libin Wang, Zimu Zhou, Bolin Ding, Lei Chen, Jieping Ye, and Ke Xu. 2017 b. Flexible Online Task Assignment in Real-Time Spatial Data. PVLDB, Vol. 10, 11 (2017), 1334--1345. Google ScholarDigital Library
- Luan Tran, Hien To, Liyue Fan, and Cyrus Shahabi. 2018. A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing. ACM Transactions on Intelligent Systems and Technology, Vol. 9, 3 (2018), 37:1--37:26. Google ScholarDigital Library
Index Terms
- Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach
Recommendations
A Survey of Spatial Crowdsourcing
Best of PODS 2017 and Regular PapersWidespread use of advanced mobile devices has led to the emergence of a new class of crowdsourcing called spatial crowdsourcing. Spatial crowdsourcing advances the potential of a crowd to perform tasks related to real-world scenarios involving physical ...
Pricing strategy for own shipping service of E-commerce platform using Two-sided market theory
Highlights- Examining how the cross-network effect influences the price decision and profit of a two-sided market platform.
AbstractRecently, many e-commerce platforms are trying to provide their own shipping services. In this context, this study suggests how to allocate the platform’s usage fees and shipping fees for maximizing profits based on the two-sided ...
Spatial and Temporal Pricing Approach for Tasks in Spatial Crowdsourcing
Web Information Systems Engineering – WISE 2020AbstractPricing is an important issue in spatial crowdsourcing (SC). Current pricing mechanisms are usually built on online learning algorithms, so they fail to capture the dynamics of users’ price preference timely. In this paper, we focus on the pricing ...
Comments