Learning to Predict Price based on E-commerce Online Auction Machine

doi:10.23940/ijpe.18.08.p29.19061912

Abstract

Abstract:

In this paper, we put forward a novel optimization framework entitled the E-commerce Online Auction Machine. Considering all the characteristics that affect online auction prices, the algorithms are applied to calculate the best fitting line to predict online auction prices by ordinary least squares. After that, regression weights are optimized using the local weighted method. Finally, using the shrinkage method, each characteristic optimal weight is obtained through the EOAM-RR algorithm. We have identified the key characteristics that affect auction prices as well as those that are not important.

Submitted on May 12, 2018; Revised on June 19, 2018; Accepted on July 22, 2018
References: 15

Xiaohui Li, Hongbin Dong, Xiaowei Wang, and Shuang Han. Learning to Predict Price based on E-commerce Online Auction Machine [J]. Int J Performability Eng, 2018, 14(8): 1906-1912.

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References 0

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