网络用户行为模式及其对信息过滤的影响
Behavior patterns of online users and the effect on information filtering
摘要：
了解基于Web的用户-对象二分网络的结构和演化是一项重要的任务，因为它们在在线信息过滤中起着基础性的作用。在本文中，我们专注于调查的模式，在线用户的行为和推荐过程中的影响。对电子商务系统的实证分析表明，用户具有显着的口味多样性和利基项目的兴趣高度重叠。此外，在真实网络和重新洗牌网络中，真实用户的行为模式可以逐渐破坏的推荐过程进行了研究。我们的研究结果表明，个性化推荐方法的性能是密切相关的真实网络结构。对每个项目的详细研究表明，对热门项目的推荐准确率几乎是最大的，相当强大的洗牌过程。然而，在去除用户的行为模式后，小众商品无法被准确推荐。我们的工作也有实际意义，因为它揭示了一个有效的方向，以提高现有的推荐系统的准确性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
