Collaborative filtering recommendation algorithm based on filling and similarity confidence factor

doi:10.3724/SP.J.1087.2013.00834

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (03): 834-837.DOI: 10.3724/SP.J.1087.2013.00834

• Network and distributed techno • Previous Articles Next Articles

Collaborative filtering recommendation algorithm based on filling and similarity confidence factor

HAO Liyan^*, WANG Jing

College of Computer Science and Technology, Huaqiao University, Xiamen Fujian 361000, China

Received:2012-09-14 Revised:2012-10-03 Online:2013-03-01 Published:2013-03-01
Contact: Li-Yan HAO
Supported by:
the National Natural Science Foundation of China

基于填充和相似性信任因子的协同过滤推荐算法

郝立燕^*,王靖

华侨大学计算机科学与技术学院,福建厦门 361000

通讯作者: 郝立燕
作者简介:郝立燕(1988-),男,山东临沂人,硕士研究生,主要研究方向:数据挖掘、推荐系统; 王靖(1981-),男,福建泉州人,副教授,博士,主要研究方向:模式识别、推荐系统。
基金资助:
国家自然科学基金资助项目(10901062); 福建省高等学校杰出青年科研人才培育计划项目(11FJPY01)。

Abstract

Abstract: In order to improve the recommendation quality of recommendation system when the data are sparse, an improved collaborative filtering algorithm was proposed. Using a data mining algorithm, the sparse rating matrix was filled firstly. Afterwards user-similarities and their confidence factors were calculated using the complete filling matrix. Ultimately, the recommendation forecast was made. Comparative experiments on typical dataset show that the algorithm is able to achieve better results even with extremely sparse data.

Key words: recommend, collaborative filtering, sparsity, matrix completion, confidence factor

摘要： 为了提高推荐系统在数据稀疏情况下的推荐质量,提出一种改进的协同过滤算法。该方法使用一种数据挖掘算法对稀疏评分矩阵进行填充; 在完整的填充矩阵上计算用户相似性,并引入相似性信任因子; 最终做出推荐预测。典型数据集上的对比实验结果表明,即使在评分数据极为稀疏的情况下,该算法仍能取得较好的结果。

关键词: 推荐, 协同过滤, 稀疏性, 矩阵填充, 信任因子

CLC Number:

TP391

HAO Liyan WANG Jing. Collaborative filtering recommendation algorithm based on filling and similarity confidence factor[J]. Journal of Computer Applications, 2013, 33(03): 834-837.

郝立燕王靖. 基于填充和相似性信任因子的协同过滤推荐算法[J]. 计算机应用, 2013, 33(03): 834-837.

References

[1]YANG J M, LI K F. An inference-based collaborative filtering approach[C]// Proceedings of the 3rd IEEE International Symposium on Dependable, Autonomic and Secure Computing. Piscataway, NJ: IEEE Press, 2007: 84-94.
[2]PARK S T, PENNOCK D M. Applying collaborative filtering techniques to movie search for better ranking and browsing[C]// Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2007:550-559.
[3]CASTRO-SCHEZ J, MIGUEL R. A highly adaptive recommender system based on fuzzy logic for B2C ecommerce portals[J]. Expert Systems with Applications, 2011(38):2441-2454.
[4]吴湖,王永吉. 两阶段联合聚类协同过滤算法[J]. 软件学报, 2011, 21(5):1042-1054.
[5]TSAI C F, HUNG C. Cluster ensembles in collaborative filtering recommendation[J]. Applied Soft Computing, 2012,12(4):1417-1425.
[6]SARWAR B, KARYPIS G, KONSTAN J. Item-based collaborative filtering recommendation algorithms [C]// Proceedings of the 10th International World Wide Web Conference. New York: ACM Press, 2001: 285-295.
[7]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.
[8]KALMAN D. A singularly valuable decomposition: the SVD of a matrix[J]. The College Mathematics Journal, 1996,27(1):2-23.
[9]LANDERMAN L R, PIEPER K C. An empirical evaluation of the predictive mean matching method for imputing missing values[J]. Sociological Methods & Research, 1997,26(1):3-33.
[10]孙小华, 陈洪, 孔繁胜. 在协同过滤中结合奇异值分解与最近邻方法[J]. 计算机应用研究, 2006,23(9):206-208.
[11]MAZUMDER R, HASTIE T, TIBSHIRANI R. Spectral regularization algorithms for learning large incomplete matrices[J]. The Journal of Machine Learning Research, 2010,11:2287-2322.
[12]陈健, 印鉴. 基于影响集的协作过滤推荐算法[J]. 软件学报, 2007,18(7):1685-1694.

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Collaborative filtering recommendation algorithm based on filling and similarity confidence factor

基于填充和相似性信任因子的协同过滤推荐算法

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