Collaborative Filtering Recommendation Based on Optimization Euclidean Distance

doi:10.3969/j.issn.1006-2475.2015.03.008

Abstract

Abstract: User evaluation data of items often are of the biodiversity and sparse characteristic in collaborative filtering recommendation system, the traditional similarity measurement algorithm cannot effectively find similar neighbors, this paper proposed a neighbor similarity computing algorithm based on optimized Euclidean distance. The algorithm introduced normalization and Jaccard similarity coefficient based on Euclidean distance calculation, and finally made the evaluation prediction and recommendation. The experiments result on typical dataset show that the algorithm can effectively improve the performance of collaborative filtering recommendation system.
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Key words: collaborative filtering, Euclidean distance, normalized, Jaccard similarity coefficient

CLC Number:

TP311

CHEN Xiao-hui, GAO Yan. Collaborative Filtering Recommendation Based on Optimization Euclidean Distance[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.03.008.

References

［1]邓晓懿,金淳,韩庆平,等. 基于情境聚类和用户评级的协同过滤推荐模型［J]. 系统工程理论与实践, 2013,33(11):2945-2953.
［2]Shi Y, Larson M, Hanjalic A. Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering［C]// Proceedings of the 2009 ACM Conference on Recommender Systems. 2009:125-132.
［3]Robert B, Yehuda K, Chris V. Modeling relationships at multiple scales to improve accuracy of large recommender systems［C]// Proceeding of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. California， USA, 2007:95-104.
［4]代金龙. 协同过滤算法中数据稀疏性问题研究［D]. 重庆:重庆大学, 2013.
［5]王道平,李秀雅,杨岑. 基于内容相似度的知识协同过滤推送算法研究［J]. 情报理论与实践, 2013,36(10):86-90.
［6]王玉斌,孟祥武,胡勋. 一种基于信息老化的协同过滤推荐算法［J]. 电子与信息学报, 2013,35(10):2391-2396.
［7]吴湖,王永吉,王哲,等. 两阶段联合聚类协同过滤算法［J]. 软件学报, 2011,21(5):1042-1054.
［8]Tsai C F, Hung C. Cluster ensembles in collaborative filtering recommendation［J]. Applied Soft Computing, 2012,12(4):1417-1425.
［9]Sarwar B， Karypis G， Konstan J. Item-based collaborative filtering recommendation algorithms［C]// Proceedings of the 10th International World Wide Web Conference. New York, 2001:285-295．
［10]赵宏霞,王新海,杨皎平. 基于用户和项目因子分析的混合协同推荐算法［J]. 计算机应用, 2011,31(5):1382-1386.
［11]李忠俊,周启海,帅青红. 一种基于内容和协同过滤同构化整合的推荐系统模型［J]. 计算机科学, 2009,36(12):142-145.
［12]李涛,王建东,叶飞跃,等. 一种基于用户聚类的协同过滤推荐算法［J]. 系统工程与电子技术, 2007,29(7):1178-1182.
［13]刘庆鹏,陈明锐. 优化稀疏数据集提高协同过滤推荐系统质量的方法［J]. 计算机应用, 2012,32(4):1082-1085.
［14]李改,潘嵘,李章凤,等. 基于大数据集的协同过滤算法的并行化研究［J]. 计算机工程与设计, 2012,33(6):2437-2441.
［15]李华,张宇,孙俊华. 基于用户模糊聚类的协同过滤推荐研究［J]. 计算机科学, 2012,39(12):83-86.
［16]于洪,李转运. 基于遗忘曲线的协同过滤推荐算法［J]. 南京大学学报(自然科学版), 2010,46(5):520-527.
［17]王海艳,张大印. 一种可信的基于协同过滤的服务选择模型［J]. 电子与信息学报, 2013,35(2):349-354.

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