Decision support and profit prediction for online auction sellers

Chia Hui Chang, Jun Hong Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, U'09 in Conjunction with KDD'09
Pages1-8
Number of pages8
DOIs
StatePublished - 2009
Event1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, U'09 in Conjunction with KDD'09 - Paris, France
Duration: 28 Jun 200928 Jun 2009

Publication series

NameProceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, U'09 in Conjunction with KDD'09

Conference

Conference1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, U'09 in Conjunction with KDD'09
Country/TerritoryFrance
CityParis
Period28/06/0928/06/09

Keywords

  • Expected profit
  • Online auction
  • Probability calibration
  • Profit prediction
  • Sample selection bias

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