Information filtering via preferential diffusion
Information filtering via preferential diffusion
复制标题
通过优先扩散进行信息过滤
DOI：
10.1103/physreve.83.066119
复制
发表时间：
2011-06-29
影响因子：
2.4
通讯作者：
Liu, Weiping
中科院分区：
文献类型：
--
作者：
Lue, Linyuan;Liu, Weiping
推荐系统在解决信息过载问题方面显示出巨大的潜力，即帮助用户在巨大的信息空间中找到感兴趣的和相关的对象。一些物理动力学，包括网络上的热传导过程和质量或能量扩散，最近在个性化推荐中找到了应用。以前的大多数研究都集中在推荐准确性上，认为这是唯一重要的因素，而忽略了多样性和新奇的重要性，这确实提供了系统的生命力。在本文中，我们提出了一个推荐算法的基础上的优惠扩散过程的用户-对象二分网络。两个基准数据集，MovieLens和Netflix的数值分析表明，我们的方法优于国家的最先进的方法。具体来说，它不仅可以提供更精准的推荐，还可以通过精准推荐冷门对象，产生更多样、更新颖的推荐。
Recommender systems have shown great potential in addressing the information overload problem, namely helping users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including the heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus overwhelmingly on recommendation accuracy as the only important factor, while overlooking the significance of diversity and novelty that indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on a user-object bipartite network. Numerical analyses on two benchmark data sets, MovieLens and Netflix, indicate that our method outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also generate more diverse and novel recommendations by accurately recommending unpopular objects.