基于模糊关系信誉模型的个性化推荐算法

Meiyu Fang, Xiaolin Zheng, Deren Chen
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引用次数: 0

摘要

针对传统协同过滤个性化推荐算法(KNN)存在冷启动、数据稀疏性、灵活性和黑盒等问题,提出了一种基于模糊信誉模型的个性化推荐算法(FRPRA)。分析了FRPRA的步骤及其与KNN的区别,探讨了FRPRA如何克服KNN存在的问题。同时,我们比较了这两种算法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Personalized Recommender Algorithm Based on Fuzzy Relation Reputation Model
For overcoming the problems that the traditional collaborative filtering personalized recommender algorithm which called KNN has such as cold-starting, data sparsity, flexibility and black box, a new personalized recommender algorithm based on fuzzy reputation model(called FRPRA) is proposed. We analyze the steps of FRPRA and the differences between it and KNN, explore the ways how FRPRA overcomes the existed problems of KNN. At the same time, we compare the performance of this two algorithms.
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