Information Filtering via Heterogeneous Diffusion in Online Bipartite Networks

Fuguo Zhang and An Zeng*, Information Filtering via Heterogeneous Diffusion in Online Bipartite Networks, [Plos One 10(6), e0129459 (2015)]

简介：推荐系统是一种有效的信息过滤手段。在经典的协同过滤算法的基础上，近年来一些基于经典物理扩散过程的算法能有效的提高推荐的精度和多样性。在用户-商品二分网中，用户节点和商品节点的结构统计特性有很大差别，这就要求推荐算法在从商品至用户方向和用户至商品方向使用不同的扩散过程。基于这个想法，本文研究了经典的热传导和物质扩散耦合算法，通过计算得到了此算法在二分网两个扩散方向的最优参数。结果显示两个扩散方向的最优参数显著不同，并且在各个扩散方向上分别使用最优参数能进一步同时提高推荐的准确性和多样性。

摘要：

The rapid expansion of Internet brings us overwhelming online information, which is impossible for an individual to go through all of it. Therefore, recommender systems were created to help people dig through this abundance of information. In networks composed by users and objects, recommender algorithms based on diffusion have been proven to be one of the best performing methods. Previous works considered the diffusion process from user to object, and from object to user to be equivalent. We show in this work that it is not the case and we improve the quality of the recommendation by taking into account the asymmetrical nature of this process. We apply this idea to modify the state-of-the-art recommendation methods. The simulation results show that the new methods can outperform these existing methods in both recommendation accuracy and diversity. Finally, this modification is checked to be able to improve the recommendation in a realistic case.

原文链接：http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129459