Analysis of readers by Classification of Articles in a News Service from Network Structures

Abstract

Information is transmitted through websites, and the immediate reaction to various information is required. Hence, the efforts for readers to select information themselves have increased, which leads to the further improve- ment of recommendation services that can reduce such burdens. On the other hand, it is pointed out that filter bubbles that only provide biased information to users are generated due to the redundant recommendation. In this research, we analyzed behavioral changes prior to recommendation by clustering, and showed that behavior changes after average number of browsing number and article type change during the period.