出版社：The Japanese Society for Artificial Intelligence
摘要：Auctions have long been used to trade goods that do not have a fixed price. In recent years, with the spread of internet auctions, the number of goods that can be handled at a single auction has increased. The time required to make a bidding decision per product remains the same even when a large number of goods are traded, such as in wholesale. For this reason, a large time is required to determine the valuation in proportion to the number of goods. This causes problems for the auctioneer and the participants. It is difficult for the auctioneer to set the reserve price and for the participants to decide the amount and target of their bids. It is necessary to understand the value of a product and estimate the end price. Since there is no fixed price for a product sold in an auction, we estimate the distribution of end prices instead of point estimation. We focus on B2B luxury brand goods auctions, where the number of goods is large and the bidding decision cost per product is high. We estimate the distribution of successful bids on actual auction data of wristwatches, which have large transaction volumes in the auction. The performance of the proposed method was measured by MAE, RMSE, and MAPE, and was close to that of experts. The proposed method was able to capture the data distribution. Finally, we show that the end price distribution estimation can be used to support both auctioneers and participants in brand-name auctions with a large number of goods.
关键词：B2B auction;hierarchical bayes;price estimation;real data analysis;auction management support
