[1]王菲,黄刚,朱峥宇.基于信任聚类的协同过滤推荐算法[J].计算机技术与发展,2019,29(05):22-26.[doi:10. 3969 / j. issn. 1673-629X. 2019. 05. 005]
　WANG Fei,HUANG Gang,ZHU Zheng-yu.Collaborative Filtering Recommendation Algorithm Based on Trust Clustering[J].,2019,29(05):22-26.[doi:10. 3969 / j. issn. 1673-629X. 2019. 05. 005]
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基于信任聚类的协同过滤推荐算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年05期
页码:
22-26
栏目:
智能、算法、系统工程
出版日期:
2019-05-10

文章信息/Info

Title:
Collaborative Filtering Recommendation Algorithm Based on Trust Clustering
文章编号:
1673-629X(2019)05-0022-05
作者:
王菲; 黄刚; 朱峥宇
南京邮电大学 计算机学院,江苏 南京 210000
Author(s):
WANG Fei; HUANG Gang; ZHU Zheng-yu
School of Computer and Software,Nanjing University of Posts and Telecommunications,Nanjing 210000,China
关键词:
推荐系统; 信任聚类; 协同过滤; 冷启动; 数据稀疏
Keywords:
recommended system; trust clustering; collaborative filtering; cold start; data sparsity
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 05. 005
摘要:
基于聚类的协同过滤算法是依靠群体的思想,根据最近邻的喜好为目标用户进行推荐,在处理规模较大的数据时是高效和可扩展的。 但传统的聚类推荐方法普遍存在准确率和覆盖率较低的问题,评分矩阵稀疏性问题也会下降其推荐性能。 针对这一系列问题,提出了一种基于信任聚类的协同过滤算法(TCCF)。 该算法使用 SVD 聚类来处理信任和不信任关系矩阵,以发现信任群体。 然后,提出了一种稀疏评分填充算法来生成密集用户评分模型解决稀疏性问题。 最后与传统协同过滤算法进行整合推荐。 开放数据测试实验表明,该算法可以有效地提高推荐的准确性和质量,并且一定程度上缓解了稀疏性问题,在聚类算法中加入信任关系,有效改善了冷启动问题,优于传统的聚类协同过滤算法。
Abstract:
The clustering-based collaborative filtering algorithm,which relies on the idea of the group and makes recommendations for target users according to the preferences of the nearest neighbor,is efficient and extensible in processing large-scale data. But traditionalclustering recommendation methods generally have low accuracy and coverage,and the sparseness of the scoring matrix will also reduceits recommendation performance. For these problems,we propose a collaborative filtering algorithm based on trust clustering (TCCF).This algorithm uses SVD clustering to process trust and distrust relationship matrices to discover trust groups. Then,we present a sparsescore filling algorithm to generate a dense user scoring model to solve the sparseness. Finally, it is integrated with the traditionalcollaborative filtering algorithm for recommendation. Open data test experiments show that this algorithm can effectively improve theaccuracy and quality of the recommendation,and alleviate the sparseness to some extent. Adding a trust relationship to the clusteringalgorithm effectively improves the cold-start problem,which is superior to the traditional clustering collaborative filtering algorithm.

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更新日期/Last Update: 2019-05-10