Abstract
We have developed a novel application called My Portal Viewer (MPV), which effectively integrates many articles collected from multiple news sites and presents these integrations through a familiar interface such as a page the user has often experienced. MPV dynamically determines the interest keywords that a user might potentially be interested in based on the articles that the user has read up to that time. And it creates categories based on these interest words dynamically. Users can, therefore, easily infer categories they are interested in from their category names, and read articles of interest. MPV and many other similar integration systems, however, have a problem that the systems cannot classify interesting articles for users and the other articles by a criteria based on frequency and cooccurence of interest words. This means that users must read too many articles in each category. We, therefore, propose a new method of selecting further articles from each category by using a user's impressions of articles. The improved MPV, called MPV Plus, selects and recommends more desirable articles using the method we propose. This paper presents the design concept and processing flow of MPV Plus, and reports how effectively it performed in evaluation experiments.
