Learning to predict price based on E-commerce Online Auction Machine

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.