Article

Price prediction and insurance for online auctions

Author:

Rayid GhaniAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 411 - 418

https://doi.org/10.1145/1081870.1081918

Published: 21 August 2005 Publication History

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Abstract

Online auctions are generating a new class of fine-grained data about online transactions. This data lends itself to a variety of applications and services that can be provided to both buyers and sellers in online marketplaces. We collect data from online auctions and use several classification algorithms to predict the probable-end prices of online auction items. This paper describes the feature extraction and selection process, and several machine learning formulations of the price prediction problem. As a prototype application, we developed Auction Price Insurance that uses the predicted end-price to offer price insurance to sellers in online auctions. We define Price Insurance as a service that offers insurance to auction sellers that guarantees a price for their goods, for an appropriate premium. If the item sells for less than the insured price, the seller is reimbursed for the difference. We show that our price prediction techniques are accurate enough to offer price insurance as a profitable business. While this paper deals specifically with online auctions, we believe that this is an interesting case study that applies to dynamic markets where the price of the goods is variable and is affected by both internal and external factors that change over time.

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Cited By

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Zec EMogren OMellquist AFallahi SAlguren P(2022)Residual value prediction using deep learning2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10021044(4560-4567)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10021044
Hatamoto KYokoyama SYamashita TKawamura H(2021)Estimating Distribution of End Prices Using Hierarchical Bayes Model in B2B Luxury Brand Goods AuctionsB2Bブランド品オークションにおける階層ベイズモデルを用いた落札価格分布の推定Transactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.36-5_AG21-C36:5(AG21-C_1-12)Online publication date: 1-Sep-2021
https://doi.org/10.1527/tjsai.36-5_AG21-C
Li XDong H(2021)A Transaction Trade-Off Utility Function Approach for Predicting the End-Price of Online Auctions in IoTWireless Communications & Mobile Computing10.1155/2021/66564212021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6656421
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Index Terms

Price prediction and insurance for online auctions
1. Computing methodologies
  1. Machine learning

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Information

Published In

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2005

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Conference

KDD05

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KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

Sponsor:
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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

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Zec EMogren OMellquist AFallahi SAlguren P(2022)Residual value prediction using deep learning2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10021044(4560-4567)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10021044
Hatamoto KYokoyama SYamashita TKawamura H(2021)Estimating Distribution of End Prices Using Hierarchical Bayes Model in B2B Luxury Brand Goods AuctionsB2Bブランド品オークションにおける階層ベイズモデルを用いた落札価格分布の推定Transactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.36-5_AG21-C36:5(AG21-C_1-12)Online publication date: 1-Sep-2021
https://doi.org/10.1527/tjsai.36-5_AG21-C
Li XDong H(2021)A Transaction Trade-Off Utility Function Approach for Predicting the End-Price of Online Auctions in IoTWireless Communications & Mobile Computing10.1155/2021/66564212021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6656421
Ye QRhuggenaath JZhang YVerwer SHilgeman M(2021)Data driven design for online industrial auctionsAnnals of Mathematics and Artificial Intelligence10.1007/s10472-020-09722-2Online publication date: 5-Jan-2021
https://doi.org/10.1007/s10472-020-09722-2
Khandelwal VChaturvedi AGupta C(2020)Amazon EC2 Spot Price Prediction Using Regression Random ForestsIEEE Transactions on Cloud Computing10.1109/TCC.2017.27801598:1(59-72)Online publication date: 1-Jan-2020
https://doi.org/10.1109/TCC.2017.2780159
Fathalla ASalah ALi KLi KFrancesco P(2020)Deep end-to-end learning for price prediction of second-hand itemsKnowledge and Information Systems10.1007/s10115-020-01495-862:12(4541-4568)Online publication date: 24-Jul-2020
https://doi.org/10.1007/s10115-020-01495-8
Zhang XShan STang SZheng HZhao BLee DSastry NWeber I(2018)Penny Auctions are PredictableProceedings of the 29th on Hypertext and Social Media10.1145/3209542.3209576(123-127)Online publication date: 3-Jul-2018
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Zhu WShih WLee YPeng WHuang J(2017)A gamma-based regression for winning price estimation in real-time bidding advertising2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258095(1610-1619)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8258095
Yang SZhao RWu FTang SGao XChen G(2016)Revenue Maximization and Contract Enforcement through Representative Bidding in Ad Auctions2016 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2016.7842229(1-6)Online publication date: Dec-2016
https://doi.org/10.1109/GLOCOM.2016.7842229
Yang BWu CSigg SZhang Y(2016)CoCo (Context vs. Content): Behavior-Inspired Social Media Recommendation for Mobile Apps2016 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2016.7841666(1-6)Online publication date: Dec-2016
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