  Information is transmitted through websites, and immediate reactions to
various kinds of information are required. Hence, efforts by users to select
information themselves have increased, which is fueling further improvements in
recommendation services that can reduce such burdens. On the other hand, filter
bubbles that only provide biased information to users are generated due to
redundant recommendations. In this research, we analyzed behavioral changes
prior to recommendation by clustering, and we found that user attributes and
cluster contents are different among users with different behavioral changes.
The proportion of users under forty and women was relatively large in the
diversity-increasing group. We also proposed an article selection model to
clarify the influence of recommendation systems on behavioral changes. We
compared our proposed model with the target data, verified it, and evaluated
the effect of recommendation systems on user behavior. Our simulation results
showed that diversity usually decreases, but collaborative filtering can
suppress the diversity decrease more effectively than non-recommendations. We
also found that the category that users are interested in the most is easily
strengthened and is one factor that leads to less diversity, and a
recommendation method that can suppress the strengthening of the category that
users are interested in the most will be effective for developing a
recommendation system that can suppress diversity decreasing.
