互联网时代所产生的海量信息使用户难以找到自己感兴趣的内容，如何进行准确且个性化的信息过滤成为广泛探讨并且亟待解决的问题。从经典的热传导算法出发，考虑产品流行度对用户选择兴趣偏好的影响，提出非平衡热传导推荐算法，并且通过引入可调参数λ，对产品流行度的影响程度进行控制。结果表明，在最优值λopt时，对于 MovieLens 系统，准确率与召回率分别提高了228．2％和228．4％；而对于 Amazon 系统，准确率与召回率分别提高了162．7％和162．8％。该算法提高了推荐的效果，表明流行度在用户作选择的过程中起到了重要作用。%In the age of Internet,the overloaded information turned the information filtering to be a widely discussed and ur-gent problem.Considering the popularity of objects,this paper presented a non-equilibrium heat conduction information filte-ring method,in which introduced a tunable parameter λto control the influence of popularity.The simulation results suggest that,with the optimal value λopt ,the precision and recall are improved by 228.2% and 228.4% respectively for the Mo-vieLens system.As to the Amazon system,those are improved by 162.7% and 162.8% respectively.The presented algorithm can improve the performance of recommendation,which suggests the importance of the popularity on users’selecting process.
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