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.