适用于校园网的视频推荐系统的设计与实现

Design and implementation of a video recommendation system in campus network

针对校园网P2P视频分享的特点,对校园网络视频推荐FP-CNVR(campus network video recommendation based on FP-growth)系统进行原型设计与实现。提出了基于顾客细分思想的数据预处理方法 CS-DP(data preprocessing based on customer segmentation),并对所使用的FP-growth算法中FP树的结构做出了优化。实验表明,与传统推荐系统相比,引进了CS-DP方法的FP-CNVR系统的推荐结果类型更为丰富,推荐结果的召回率提高了一半并保持了准确率基本稳定。

According to the feature of video-sharing with P2 P in campus network, a video recommendation system in campus network called FP-CNVR system was designed and implemented; a novel data cleaning method based on customer segmentation was proposed; the structure of FP-tree in the process of frequent pattern mining with FP-growth algorithm was improved. Experimental results show that the novel recommendation system can provide more reliable recommended results, and also the novel data cleaning method can improve the accuracy of the recommendation results. The research may have some inspiration to related subjects in campus network.