基于优先扩散的信息过滤
Information filtering via preferential diffusion
摘要：
推荐系统在解决信息过载问题上显示出巨大的潜力，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，包括热传导过程和网络上的质量或能量扩散，最近在个性化推荐中找到了应用。以往的研究大多集中在推荐准确率作为唯一的重要因素，而忽略了多样性和新颖性的意义，这确实提供了系统的生命力。本文提出了一种基于用户-对象二部网络偏好扩散过程的推荐算法。在两个基准数据集MovieLens和Netflix上的数值分析表明，我们的方法优于最先进的方法。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。
英文摘要：
Recommender systems have shown great potential to address information overload problem, namely to help users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including 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 overlook the significance of diversity and novelty which indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on user-object bipartite network. Numerical analyses on two benchmark datasets, 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.
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。
