A support system for predicting eBay end prices

Decision Support Systems

Volumn 44, Issue 4, 2008, Pages 970-982

  van Heijst, Dennis ^a   Potharst, Rob ^a   van Wezel, Michiel ^a  

^a Erasmus University Rotterdam   (Netherlands)

Author keywords

Boosting; eBay; Electronic auctions; Text mining

Indexed keywords

ALGORITHMS; DATA MINING; LEARNING SYSTEMS; NUMERICAL METHODS;

BOOSTING ALGORITHMS; EBAY END PRICES; ELECTRONIC AUCTIONS; SELLER FEEDBACK RATING; TEXT MINING;

DECISION SUPPORT SYSTEMS;

EID: 38649116634     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2007.11.004     Document Type: Article

References (31)

1. Atif Y. Building trust in e-commerce. IEEE Internet Computing 6 1 (2002) 18-24 Jan/Feb
2. Ba S. Establishing online trust through a community responsibility system. Decision Support Systems 31 3 (August 2001) 323-336
3. Ba S., Whinston A.B., and Zhang H. Building trust in online auction markets through an economic incentive mechanism. Decision Support Systems 35 3 (June 2003) 273-286
4. Ba S., and Pavlou P. Evidence of the effect of trust building technology in electronic markets: price premiums and buyer behavior. MIS Quarterly 26 3 (2002) 243-268
5. Bajari P., and Hortacsu A. Economic insights from internet auctions. Journal of Economic Literature 42 2 (2004) 457-486
6. Breiman L. Bagging predictors. Machine Learning 24 (1996) 123-140
7. Breiman L. Arcing classifiers. Annals of Statistics 26 2 (1998) 801-824
8. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Classification and Regression Trees. 2nd Edition (1996), Chapman & Hall, NY
9. Bryan D., Lucking-Reiley D., Prasad N., and Reeves D. Pennies from eBay: The Determinants of Price in Online Auctions. Working Papers 0003, Department of Economics (November 1999), Vanderbilt University (May 2006 Version)
10. eBay- Investor Relations. Web-site, 2006. http://investor.ebay.com/. Accessed on 6/20/2006.
11. Freund Y., and Schapire R.E. Experiments with a new Boosting Algorithm. In: Saitta L. (Ed). Proceedings of the 13th International Conference on Machine Learning, pages (1996), Morgan Kaufmann, San Francisco 148-156
12. Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55 1 (1997) 119-139
13. Friedman J.H. Greedy function approximation: a gradient boosting machine. Annals of Statistics 29 5 (2001) 1189-1232 October
14. Ghani R., and Simmons H. Predicting the end-price of online auctions. Proceedings of International Workshop on Data Mining and Adaptive Modelling Methods for Economics and Management held in conjunction with the 15th European Conference on Machine Learning (ECML/PKDDD 2004) (2004) (Pisa, Italy/)
15. Gregg D.G., and Walczak S. Auction advisor: an agent-based online auction decision Support System. Decision Support Systems 41 2 (2006) 449-471
16. HarvEX / Turbo Sniper software. Web-site, 2007. http://www.xellsoft.com/HarvEX.html. Accessed on 6/25/2007.
17. Jones A.J.I. On the concept of trust. Decision Support Systems 33 3 (2002) 225-232 July
18. Krishna V. Auction Theory (2002), Academic Press/Elsevier Science. 0-12-426297-X
19. McCallum A., and Nigam K. A comparison of event models for naive Bayes text classification. Proceedings of AAAI-98, Workshop on Learning for Text Categorization (1998)
20. Nasukawa T., and Nagano T. Text analysis and knowledge mining system. IBM Systems Journal 40 4 (2001) 967-984
21. Olson J.S., and Olson G.M. i2i trust in e-commerce. Communications of the ACM 43 (December 2000) 41-44
22. Porter M.F. An algorithm for suffix stripping. Program 14 3 (1980) 130-137
23. R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing (2005). 3-900051-07-0. http://www.R-project.org (Vienna, Austria) http://www.R-project.org
24. Resnick P., Zeckhauser R., Swanson J., and Lockwood K. The value of reputation on eBay: a controlled experiment. Experimental Economics 9 2 (2006) 79-101 June
25. Brian Ripley D. Pattern Recognition and Neural Networks (1996), Cambridge University Press, Cambridge
26. Roth A.E., and Ockenfels A. Last-minute bidding and the rules for ending second-price auctions: evidence from eBay and Amazon auctions on the internet. American Economic Review 92 4 (2002) 1093-1103 September
27. Salton G. Automatic Text Processing: The Transformation Analysis and Retrieval of Information by Computer (1989), Addison-Wesley. 0201122278
28. Slattery S., and Craven M. Combining statistical and relational methods for learning in hypertext domains. In: Page D. (Ed). Proceedings of ILP-98, 8th International Conference on Inductive Logic Programming (1998), Springer Verlag, Madison, US 38-52 (Heidelberg, DE. Published in the "Lecture Notes in Computer Science" series, number 1446)
29. T.M. Therneau, B. Atkinson, B. Ripley. rpart: Recursive Partitioning, 2005. R package version 3.1-22, S-PLUS 6.x original at http://www.mayo.edu/hsr/Sfunc.html.
30. Shanshan Wang, Wolfgang Jank, Galit Shmueli. Explaining and forecasting online auction prices and their dynamics using functional data analysis. Journal of Business and Economic Statistics, in press.
31. Weiss S.M., Apté C., Damerau F.J., Johnson D.E., Oles F.J., Goetz T., and Hampp T. Maximizing text-mining performance. IEEE Intelligent Systems 14 4 (1999) 63-69