Analogy of the Degree of Interest in a News Article by Gaze Information

Abstract

There is a huge amount of news information on the Internet. This gives users the advantage of having access to a large amount of information. However, users are not always interested in all the news that is distributed. Therefore, when users read the news on the Internet, they need to select the information they are interested in. We have developed NEAR, a news application for smartphones, with the aim of solving the problem of information overload. However, in order for the app to learn the user’s interest trends, it needed to have the user sort through a lot of news, which was a burden for the user. Therefore, we propose a method that focuses on the user’s gaze information as a method for learning interests while reducing the burden of article selection by the user. By focusing on gaze, it is thought that it will be possible to analogize the degree of interest on a word-by-word basis rather than on an article-by-article basis, and it is expected that the number of article selection required for learning will be reduced.