Dynamically adaptive hybrid intelligent collaborative filtering recommendation algorithm

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (12): 3487-3490.

Previous Articles Next Articles

Dynamically adaptive hybrid intelligent collaborative filtering recommendation algorithm

CHEN Xiaoyu

College of Computer and Information Engineering, Nanyang Institute of Technology, Nanyang Henan 473000, China

Received:2014-07-07 Revised:2014-09-03 Online:2014-12-31 Published:2014-12-01
Contact: CHEN Xiaoyu

动态自适应的混合智能协同推荐算法

陈小玉

南阳理工学院计算机与信息工程学院,河南南阳 473000

通讯作者: 陈小玉
作者简介:陈小玉(1978-),女,河南南阳人,副教授,硕士,CCF会员, 主要研究方向:智能计算、数据挖掘、机器学习。
基金资助:
河南省重点科技攻关计划项目

Abstract

Abstract:

In order to solve the problems of current collaborative filtering algorithm,such as sparse data, inconspicuous user interest changes, timeliness and poor recommendation quality, an adaptive hybrid intelligent algorithm was proposed. The initial neighbor set of the target user was got by modified kernel fuzzy clustering analysis firstly, which reduced the calculation range; furthermore, the initial equivalence relation and equivalence relation similarity were redefined, and a dynamic x nearest neighbor algorithm was proposed to get the accurate neighbor set, and then to fill the matrix using the prediction score, which optimized score data quality. At last, the interest change factor and rating time weight of the users was introduced, and mined potential interest changes to obtain better recommendation. The experimental results show that the algorithm can get more accurate nearest neighbor set, which can improve the prediction accuracy and the quality of recommendation, and provide better personalized recommendation for users.

摘要：

针对当前协同过滤推荐算法存在数据稀疏、用户兴趣变化和时效性不明显、推荐质量差等问题,提出了一种动态自适应的混合智能协同过滤推荐算法。首先利用修正核模糊聚类算法进行聚类分析,得到目标用户初始邻居集,缩小计算范围；重新定义了初始等价关系和等价关系相似性,提出了动态x近邻算法,得到准确邻居集并用预测评分填充矩阵,优化数据质量；最后引入用户兴趣变化因子和评价时效,挖掘用户潜在的兴趣变化,得到较好的推荐结果。实验结果表明,该算法能够得到更准确的最近邻居集,提高预测准确率和推荐质量,为用户提供更好的个性化推荐。

CLC Number:

TP391

CHEN Xiaoyu. Dynamically adaptive hybrid intelligent collaborative filtering recommendation algorithm[J]. Journal of Computer Applications, 2014, 34(12): 3487-3490.

陈小玉. 动态自适应的混合智能协同推荐算法[J]. 计算机应用, 2014, 34(12): 3487-3490.

References

［1］FU A W, KWONG W, FU R, et al. Mining N-most interesting itemsets［J］. Methodologies for Intelligent Systems, 2000, 12(4): 41-48.
［2］BOBADILLA J, HERNANDO A, ORTEGA F, et al. Collaborative filtering based on significances［J］. Information Sciences, 2012, 185(1): 1-17.
［3］KIM N H, EI-SADDIK A, JO G S. Collaborative error-reflected models for cold-start recommender systems［J］. Decision Support Systems, 2011, 51(3): 519-531.
［4］ZHANG F, CHANG J, ZHOU Q. Context-aware recommendation algorithm based on fuzzy C-means clustering［J］. Journal of Computer Research and Development, 2013,50(10): 2185-2194.(张付志,常俊风,周全强.基于模糊C均值聚类的环境感知推荐算法［J］.计算机研究与发展,2013,50(10):2185-2194.)
［5］CHOI K, SUH Y. A new similarity function for selecting neighbors for each target item in collaborative filtering［J］．Knowledge-Based Systems,2013,37(5):146-153.
［6］HUANG C, YIN J, WANG J, et al. Uncertain neighbors’ collaborative filtering recommendation algorithm［J］. Chinese Journal of Computers, 2010, 33(8): 1369-1377.(黄创光,印鉴,汪静,等.不确定近邻的协同过滤推荐算法［J］.计算机学报,2010,33(8):1369-1377.)
［7］SUN L, LI J, SUN H. SlopeOne collaborative filtering recommendation algorithm based on dynamic k-nearest-neighborhood［J］.Journal of Computer Science and Exploration,2011,5(9): 857-864.(孙丽梅,李晶皎,孙焕良.基于动态k近邻的SlopeOne协同过滤推荐算法［J］.计算机科学与探索,2011,5(9):857-864.)
［8］DENG J. Study of collaborative filtering recommendation algorithm based on users interest change［J］. Journal of Wuhan Polytechnic University, 2013,32(4): 48-51.(邓娟.基于用户兴趣变化的协同过滤推荐算法［J］.武汉工业学院学报,2013,32(4):48-51.)
［9］LI G, ZHANG Z, LIU F, et al. Nonlinear combinatorial collaborative filtering recommendation algorithm［J］. Journal of Computer Applications, 2011,31(11): 3063-3067.(李国,张智斌,刘芳先,等.非线性组合的协同过滤推荐算法［J］.计算机应用,2011,31(11):3063-3067.)
［10］XING Z, LIANG J, ZHU Q. Multi-dimensional adaptive collaborative filtering recommendation algorithm［J］. Journal of Chinese Computer Systems, 2011,32(11): 2210-2216.(邢哲,梁竞帆,朱青.多维度自适应的协同过滤推荐算法［J］.小型微型计算机系统,2011,32(11):2210-2216.)
［11］LI P. Optimized implementation of hybrid recommendation algorithm［J］. Computer Science,2014,41(2):68-71.(李鹏飞.基于混合模型推荐算法的优化［J］.计算机科学,2014,41(2):68-71.)
［12］ZHAO G. Fuzzy clustering algorithm with modified kernel functions［J］. Journal of Computer Applications, 2010, 30(7): 1926-1929.(赵国亮.修正核函数模糊聚类算法［J］.计算机应用,2010,30(7):1926-1929.)
［13］LENG Y, LIANG C. Method of neighborhood formation in collaborative filtering［J］. Pattern Recognition and Artificial Intelligence, 2013,26(10):968-974.(冷亚军,梁昌勇.协同过滤中一种有效的最近邻选择方法［J］.模式识别与人工智能,2013,26(10):968-974.)
［14］AN Q, SHEN J, WANG G. A clustering method based on information granularity and rough sets［J］. Pattern Recognition and Artificial Intelligence, 2003, 16(4): 412-417.(安秋生,沈钧毅,王国胤.基于信息粒度与Rough集的聚类方法研究［J］. 模式识别与人工智能,2003,16(4): 412-417.)
［15］ZHAO Y, ZHOU X, HE X, et al. An effective high attribute dimensional sparse clustering［J］. Pattern Recognition and Artificial Intelligence, 2006, 19(3): 289-294.(赵亚琴,周献中,何新,等.一种有效的高属性维稀疏数据聚类算法［J］. 模式识别与人工智能, 2006, 19(3): 289-294.)

[1]	. Pavement defect detection algorithm with enhanced morphological perception [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	YANG Jianfeng, CHEN Bin, LI Yuxuan. Self-supervised point cloud anomaly detection method based on point cloud reconstruction [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	XUE Zhenhua, LI Qiang, HUANG Chao. Vision foundation model-driven pixel-level image anomaly detection model [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Binocular vision object location algorithm for robot arm grasping [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	. Multi-attention contrastive learning for infrared small target detection [J]. Journal of Computer Applications, 0, (): 0-0.
[6]	LI Zhonghua, ZHONG Gengxin, FAN Ping, ZHU Hengliang. Camouflaged object detection by boundary mining and background guidance [J]. Journal of Computer Applications, 0, (): 0-0.
[7]	. Multi-view difficult airway recognition based on discriminant region guidance [J]. Journal of Computer Applications, 0, (): 0-0.
[8]	. Privacy preserving localization of surveillance images based on large vision models [J]. Journal of Computer Applications, 0, (): 0-0.
[9]	. Gravity data denoising method based on multilevel wavelet residual network#br# #br# [J]. Journal of Computer Applications, 0, (): 0-0.
[10]	. Data augmentation method for abnormal passenger behavior in elevators based on dynamic graph convolutional network [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	. Text-based person retrieval method based on multi-granularity shared semantic center association [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	ZHANG Qing, YANG Fan, FANG Yuhan. Chinese Spelling Correction Algorithm Based on Multi-Modal Information Fusion [J]. Journal of Computer Applications, 0, (): 0-0.
[13]	WANG Hao, WANG Jinwei, CHENG Xin, ZHANG Jiawei, WU Hao, LUO Xiangyang, MA Bin. A Review of JPEG Recompression Forensics for Color Images [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	. Distribution adaptation and dynamic curriculum pseudo-labeling framework for semi-supervised fire detection [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	. Multimodal harmful content detection method based on weakly supervised modality semantic enhancement [J]. Journal of Computer Applications, 0, (): 0-0.

Dynamically adaptive hybrid intelligent collaborative filtering recommendation algorithm

动态自适应的混合智能协同推荐算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics