Improving a News Recommendation System in Adapting to Interests of a User with Storage of a Constant Size

It is desired to have a system that can recommend news articles according to interests of a user, which would change with time. In this paper, we attempt to improve a news recommendation system with supervised classification by integrating a clustering method into the system in order to adapt flexibly to the variety of interests of a user. To follow the changes of interests with time, we need to make not only the classifier but also the clustering module of the system be easily updatable. We construct clusters in the feature space from combining one-dimensional clusters to make the size of storage to hold for updating clusters be constant. We let the data distribution in each one-dimensional space be influenced by the clustering results from another one-dimensional space, thereby taking into account of data distribution in the original multiple dimensional space. Main contribution of this paper is to propose a method that can achieve both of the two goals: to improve the performance of a news recommendation system and to make the amount of data to hold for updating clusters be constant. Some experimental results are shown and the effective and weak points of our proposal are discussed.