研究成果：Bolun Chen, An Zeng* and Lin Chen, The effect of heterogeneous dynamics of online users on information filtering, Physics Letters A 379, 2839 (2015).

简介：信息过滤最常用的算法验证方法是将实际数据随机划分为训练集和测试集。然而，这一操作的隐含假设是不同在线用户的每条连边是同质的。本文在数据集划分过程中考虑了在线用户行为的异质性，提出了基于用户活跃度的数据集划分方法。本文发现，若将活跃用户的测试集规模减小并将非活跃用户的测试集规模增加，大部分个性化推荐算法的推荐效果都将提升。这一发现将指导商家更好的运用推荐系统，应当更频繁的更新活跃用户的推荐列表。

Abstract：The rapid expansion of the Internet requires effective information filtering techniques to extract the most essential and relevant information for online users. Many recommendation algorithms have been proposed to predict the future items that a given user might be interested in. However, there is an important issue that has always been ignored so far in related works, namely the heterogeneous dynamics of online users. The interest of active users changes more often than that of less active users, which asks for different update frequency of their recommendation lists. In this paper, we develop a framework to study the effect of heterogeneous dynamics of users on the recommendation performance. We find that the personalized application of recommendation algorithms results in remarkable improvement in the recommendation accuracy and diversity. Our findings may help online retailers make better use of the existing recommendation methods.

原文链接：http://www.sciencedirect.com/science/article/pii/S0375960115008063