  Understanding the structure and evolution of web-based user-object bipartite
networks is an important task since they play a fundamental role in online
information filtering. In this paper, we focus on investigating the patterns of
online users' behavior and the effect on recommendation process. Empirical
analysis on the e-commercial systems show that users have significant taste
diversity and their interests for niche items highly overlap. Additionally,
recommendation process are investigated on both the real networks and the
reshuffled networks in which real users' behavior patterns can be gradually
destroyed. Our results shows that the performance of personalized
recommendation methods is strongly related to the real network structure.
Detail study on each item shows that recommendation accuracy for hot items is
almost maximum and quite robust to the reshuffling process. However, niche
items cannot be accurately recommended after removing users' behavior patterns.
Our work also is meaningful in practical sense since it reveals an effective
direction to improve the accuracy and the robustness of the existing
recommender systems.
