抄録
Along with the recent developments in information technology, purchasing on E-commerce (EC) sites has become popular and Web marketing measures are growing in significance. However, conversion rates on EC sites are usually not high, so there is room for improvement in Web marketing. In general, a user browses each page on an EC site before he/she purchases an item, and in many cases, the user leaves the EC site without purchasing. Therefore, constructing a binary classification model with browsing paths is considered here to obtain some findings for an effective Web marketing strategy. This model learns the customers' browsing history data with their purchase and non-purchase labels that are accumulated on an EC site. It is generally considered that there are different page transition tendencies between purchasing and non-purchasing users on an EC site. This study focuses on the difference in page transition patterns between purchasing and non-purchasing users. To model customers' page transition behaviors, N-gram and mutual information are introduced to select features from browsing history data. In addition, a binary classification experiment using real browsing history data was carried out and the effectiveness of the proposed method is demonstrated.
