基于关键用户的非均衡热传导与物质扩散算法

物质扩散和热传导算法作为2个经典的基于网络结构的算法被普遍用于信息过滤.然而，现有的算法将所有的用户等同看待，没有区分不同用户的贡献，也没有移除恶意用户，从而导致性能不理想.针对这一问题提出基于关键用户的非均衡热传导与物质扩散算法（core-UHM），用以解决教育资源库中的高质量资源检索问题.算法的背后思想主要是基于信息领域中的帕累托法则，也即是20%的用户主导着整个系统中80%的交互.基于关键用户不仅能够保证资源的质量，同时也大大节省了计算开销.通过在真实的学术搜索日志数据集上的实验，证明了算法的有效性.

Core-User-Group-Based Unbalanced Heat Conduction and Mass Diffusion Algorithm

Mass diffusion and heat conduction algorithms are commonly used as two classic network structure-based algorithms for information filtering. However, the existing algorithms treat all users equally without distinguishing the contributions of different users and removing malicious users, which results in poor performance. In response to the above problems, we propose a core-user-group-based unbalanced heat conduction and mass diffusion algorithm (core-UHM). The underlying idea is mainly based on the Pareto Principle in the information field, that is, 20% of users dominate 80% of the interaction in the whole system. Based on core users, not only can the quality of resources be guaranteed, but also the computational overhead is greatly saved. Experiments on real academic search log data sets demonstrate the effectiveness of our proposed algorithm.