A method for improving a visualized preference transition network by screening nodes in the network, where a node represents a product item, is described. The original preference transition network was developed not only for visualizing customer movements/trends in selecting items but also for finding the features of items. However, understanding such movements/trends and features is difficult when the network has many nodes and links. To solve this problem, the proposed method is a sensitivity analysis for identifying redundant nodes and links with adjustment of the threshold expressed by the Simpson coefficient. The effectiveness of this method was shown through a numerical experiment for 172 kinds of products, 2,227 customers, and their 90,000 sales records.
['Yoshihiro Hayashi', 'Hiroshi Tsuji', 'Ryosuke Saga']
Visualizing method based on item sales records and its experimentation