@inproceedings{oai:kumadai.repo.nii.ac.jp:00024562,
 author = {Nishitarumizu, Akito and 糸川, 剛 and Itokawa, Tsuyoshi and Kitasuka, Teruaki and Aritsugi, Masayoshi and 西垂水, 彬人 and 糸川, 剛 and 北須賀, 輝明 and 有次, 正義},
 book = {Advances in Web Technologies and Applications - Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010},
 month = {Apr},
 note = {application/pdf, 論文(Article), 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., http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5474146&abstractAccess=no&userType=inst},
 pages = {109--115},
 publisher = {IEEE},
 title = {Improving a News Recommendation System in Adapting to Interests of a User with Storage of a Constant Size},
 year = {2010},
 yomi = {イトカワ, ツヨシ}
}