计算机与现代化 ›› 2013, Vol. 1 ›› Issue (9): 50-53.doi: 10.3969/j.issn.1006-2475.2013.09.012

• 人工智能 • 上一篇    下一篇

基于信任用户联合聚类的协同过滤算法

王宗武   

  1. 广东工业大学计算机学院,广东 广州 510006
  • 收稿日期:2013-03-28 修回日期:1900-01-01 出版日期:2013-09-17 发布日期:2013-09-17

A Collaborative Filtering Algorithm Based on Clustering of Users Trust

WANG Zong-wu   

  1. Faculty of Computer, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2013-03-28 Revised:1900-01-01 Online:2013-09-17 Published:2013-09-17

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。

关键词: 推荐系统, 协同过滤, 聚类, 用户信任

Abstract: 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.

Key words: recommender system, collaborative filtering, clustering, users trust

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