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基于信任用户联合聚类的协同过滤算法 被引量：1

A Collaborative Filtering Algorithm Based on Clustering of Users Trust
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摘要 目前大量的协同过滤算法由于用户量过大存在速度瓶颈问题,由于新用户的加入导致冷启动问题。本文提出一种结合用户信任度和用户兴趣进行聚类的协同过滤算法。该算法综合用户信任度以及用户评分相似性来进行聚类,在聚类结果中寻找最近邻居并产生推荐。实验结果表明,该方法不仅加快了推荐结果的产生,而且还提高了推荐精度。 At present,the large number of users has led to speed bottleneck problem,and the addition of new users has led to the cold start problem in collaborative filtering algorithm.A collaborative filtering algorithm is proposed based on clustering of the users trust and interests.The algorithm clusters the users based on their trust degree and scoring similarity.The experimental results show that,this method not only accelerates the results recommended,but also improves the accuracy of recommendation.
作者 王宗武
机构地区 广东工业大学计算机学院
出处 《计算机与现代化》 2013年第9期50-53,共4页 Computer and Modernization
关键词 推荐系统 协同过滤 聚类 用户信任 recommender system collaborative filtering clustering users trust
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献15

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二级参考文献23

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计算机与现代化
2013年 第9期

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