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Decision support and profit prediction for online auction sellers

Published: 28 June 2009 Publication History

Abstract

Online auction has become a very popular e-commerce transaction type. The immense business opportunities attract a lot of individuals as well as online stores. With more sellers engaged in, the competition between sellers is more intense. For sellers, how to maximize their profit by proper auction setting becomes the critical success factor in online auction market. In this paper, we provide a selling recommendation service which can predict the expected profit before listing and, based on the expected profit, recommend the seller whether to use current auction setting or not. We collect data from five kinds of digital camera from eBay and apply machine learning algorithm to predict sold probability and end-price. In order to get genuine sold probability and end-price prediction (even for unsold items), we apply probability calibration and sample selection bias correction when building the prediction models. To decide whether to list a commodity or not, we apply cost-sensitive analysis to decide whether to use current auction setting. We compare the profits using three different approaches: probability-based, end-price based, and our expected-profit based recommendation service. The experiment result shows that our recommendation service based on expected profit gives higher earnings and probability is a key factor that maintains the profit gain when ultra cost incurs for unsold items due to stocking.

References

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R. Ghani and H. Simmons. Predicting the end-price of online auctions. In Proceedings of the International Workshop on Data Mining and Adaptive Modelling Methods for Economics and Management, Pisa, Italy, 2004.
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J. Heckman. Sample selection bias as a specification error. Econometrica, (47):153--161, 1979.
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D. Heijst, R. Potharst, and M. Wezel. A support system for predicting ebay end prices. Technical report, Econometric Institute Report, 2006.
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J. Platt. Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. Advances in Large Margin Classifiers, pages 61--74, 1999.
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A. Smith and C. Elkan. A bayesian network framework for reject inference. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 286--295, 2004.
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S. Wang, W. Jank, and G. Shmueli. Forecasting ebayąęs online auction price using functional data analysis. Journal of Business and Economic Statistics, 2006.
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B. Zadrozny. Learning and evaluating classifiers under sample selection bias. In Proceedings of the 21th International Conference on Machine Learning, 2004.
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B. Zadrozny and C. Elkan. Learning and making decisions when costs and probabilities are both unknown. In Proceedings of the Seventh ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), pages 204--213, 2001.

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  • (2016)Machine learning approach for predicting end price of online auction2016 International Conference on Inventive Computation Technologies (ICICT)10.1109/INVENTIVE.2016.7830232(1-5)Online publication date: Aug-2016

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cover image ACM Conferences
U '09: Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
June 2009
66 pages
ISBN:9781605586755
DOI:10.1145/1610555
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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Publication History

Published: 28 June 2009

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Author Tags

  1. expected profit
  2. online auction
  3. probability calibration
  4. profit prediction
  5. sample selection bias

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  • (2016)Machine learning approach for predicting end price of online auction2016 International Conference on Inventive Computation Technologies (ICICT)10.1109/INVENTIVE.2016.7830232(1-5)Online publication date: Aug-2016

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