Abstract
It is very important that sellers provide reasonable reserve prices for auction items in internet auction systems. We recently have proposed the agent based system in order to generate reserve prices automatically, based on the case similarity of information retrieval theory and the moving average of time series analysis. However, the approaches have some problems that they can not reflect the recent trend of auction prices on the generated reserve prices properly, because they only provide the bid prices or average prices of items, by using the past auction data for sellers. In this paper, in order to overcome the problems, we propose Similarity-Based Time Series Analysis which search the past bid price through case similarity and then generate reserve prices by using time series analysis. Through performance experiments, we show that the successful bid rate of the new method can be improved by preventing sellers from making unreasonable reserve prices compared with the past methods.
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References
Carbone, H.C.: Forecasting Trends and Seasonal by Exponentially Weighted Moving Averages. Carnegie Institute of Technology 2(52) (1957)
Frakes, W.B.: Information Retrieval- Data Structures & Algorithms. Prentice-Hall, Englewood Cliffs (1992)
Drucker, P.F., et al.: Accurate Business Forecasting. Harvard Business School Press (1991)
Heck, E.V., Vervest, P.: How Should CIO’s Deal with Web-Based Auctions? Communications of the ACM 41(7), 99–100 (1998)
Jones, C.P.: Investments-Analysis and Management, 8th edn. Wiley, Chichester (2001)
Ko, M.J., Lee, Y.K.: Automatic Reserve Price Generation for an Internet Auction System Using Moving Average. Journal of Society for e-Business Studies 9(2), 17–31 (2004)
LeBaron, B.: Do Moving Average Tradiule Imply Nonlinearities in Foreign Exchange Markets? Technical Report 9222, Social Systems Research Institute, Univ. of Wisconsin, Madison (1992)
Lee, Y.K., Kim, S.W., Ko, M.J., Park, S.E.: Pricing Agents for a Group Buying System. In: Shafazand, H., Tjoa, A.M. (eds.) EurAsia-ICT 2002. LNCS, vol. 2510, pp. 693–700. Springer, Heidelberg (2002)
Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Application. Springer, Heidelberg (2002)
Tashman, L.J.: Out-of-sample Tests of Forecasting Accuracy: an Analysis and Review. Int’l Journal of Forecasting 16(4), 437–450 (2000)
eBay Auction Guideline (2003), http://webhelp.ebay.com/cgi-bin/eHNC
Korea Encar List (2004), http://www.encar.co.kr/proc23/23000.jsp
Korea Glovis Secondhand Trend (2004), http://www.hkaa.co.kr/Sise/SerCond01.aspx
Korea Cellphone Site (2004), http://used4989.com/home2
Korea Phonemore Site (2004), http://www.phonemoa.co.kr
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© 2006 Springer-Verlag Berlin Heidelberg
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Ko, M.J., Lee, Y.K. (2006). Reserve Price Recommendation by Similarity-Based Time Series Analysis for Internet Auction Systems. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_36
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DOI: https://doi.org/10.1007/11892960_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
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