在线拍卖商品最终成交价格预测

Prediction of the End-price of Online Auction Item

在线拍卖产生了大量的电子数据,充分利用这些数据,无论对于卖者、买者还是站点管理员,都会产生极大的作用。卖者通过预测最终的成交价格可以优化自己拍卖商品的拍卖价格。在线拍卖中的大量属性是动态变化的,以至于不能直接运用机器学习算法对最终成交价格进行预测。为了解决这个问题,收集了大量的在线拍卖成交数据,提取和构造了维度固定的特征列表,运用BP算法进行最终成交价格的预测,并对预测结果进行了分析。

The activities of online auction produces a large number of transaction data. If utilized properly, these data can be of great benefit to sellers, buyers and site administrator. Typically, prediction result may help sellers optimize the selling price of their items. Thus, transaction time can be shorted and cost can be saved. This paper collects a lot of historical exchange data from Eachnet, an online auction website most famous in China, and uses machine learning algorithms to forecast the final price of auction items. On the basis of the categorization and preprocessing of data, the prediction is made. The prediction results and performances are discussed to verily the proposed solutions.