Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (6): 102-109    DOI: 10.11925/infotech.2096-3467.2018.0017
Current Issue | Archive | Adv Search |
Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor
Wang Daoping, Jiang Zhongyang(), Zhang Boqing
Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
Download: PDF (683 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper presents a collaborative filtering algorithm based on gray correlation analysis and time factor, aiming to address the low similarity resolvability and user’s interest drifting issues of the traditional algorithms. [Methods] First, we proposed a new method to calculate user similarity based on gray relational degree. Then, we used the time weight function to improve the Pearson correlation coefficients. Third, we created a hybrid similarity calculation method and made recommendation based on the neighbors of the target user. Finally, we used the MovieLens dataset to examine the new algorithm. [Results] Compared with the traditional collaborative filtering algorithms and those considering gray correlation analysis or time factor alone, the proposed algorithm reduced the mean absolute error (MAE). [Limitations] It takes the proposed algorithm longer time to calculate the hybrid similarity. [Conclusions] The hybrid similarity method improves the accuracy of recommended items for the target users and has a very good commercial promotion prospect.

Key wordsGray Correlation Analysis      Time Factor      Collaborative Filtering      Hybrid Similarity     
Received: 04 January 2018      Published: 11 July 2018
ZTFLH:  F270 G35  

Cite this article:

Wang Daoping,Jiang Zhongyang,Zhang Boqing. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor. Data Analysis and Knowledge Discovery, 2018, 2(6): 102-109.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0017     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I6/102

t
1i
1 2 …… n
12(t) 0 12(2) …… 12(n)
13(t) 0 13(2) …… 13(n)
…… …… …… ……
1n(t) 0 1n(2) …… 1n(n)
t
r1i
1 2 …… n
r12(t) 1 r12(2) …… r12(n)
r13(t) 1 r13(2) …… r13(n)
…… …… …… ……
r1n(t) 1 r1n(2) …… r1n(n)
[1] Ricci F, Rokach L, Shapira B, et al.Recommender Systems Handbook[M]. Berlin: Springer, 2011: 145-186.
[2] Ariyoshi Y, Kamahara J.A Hybrid Recommendation Method with Double SVD Reduction[C]// Proceedings of International Conference on Database Systems for Advanced Applications Database System for Advanced Applications, 2010: 365-373.
[3] Wang S, Xie Y, Fang M.A Collaborative Filtering Recommendation Algorithm Based on Item and Cloud Model[J]. Wuhan University Journal of Natual Sciences, 2011, 16(1): 16-20.
doi: 10.1007/s11859-011-0704-4
[4] Ma T, Guo L, Tang M, et al.A Collaborative Filtering Recommendation Algorithm Based on Hierarchical Structure and Time Awareness[J]. IEICE Transactions on Information & Systems, 2016, 99(6): 1512-1520.
doi: 10.1587/transinf.2015EDP7380
[5] 朱思丞, 黄瑛, 孙志锋. 推荐算法时间动态特性研究进展[J]. 工业控制计算机, 2015, 28(8): 99-100.
[5] (Zhu Sicheng, Huang Ying, Sun Zhifeng.Research on Progress of Time-based Dynamic Recommender System[J]. Industrial Control Computer, 2015, 28(8): 99-100.)
[6] Zhang X L, Lee T M D, Pitsilis G. Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering[J]. Journal of Computer Science and Technology, 2013, 28(4): 616-624.
doi: 10.1007/s11390-013-1362-0
[7] Xia C, Jiang X, Liu S, et al.Dynamic Item-based Recommendation Algorithm with Time Decay[C]// Proceedings of International Conference on Natural Computation (ICNC 2010). 2010: 242-247.
[8] 董立岩, 王越群, 贺嘉楠, 等. 基于时间衰减的协同过滤推荐算法[J]. 吉林大学学报: 工学版, 2017, 47(4): 1268-1272.
doi: 10.13229/j.cnki.jdxbgxb201704036
[8] (Dong Liyan, Wang Yuequn, He Jia’nan, et al.Collaborative Filtering Recommendation Algorithm Based on Time Decay[J]. Journal of Jilin University: Engineering and Technology Edition, 2017, 47(4): 1268-1272.)
doi: 10.13229/j.cnki.jdxbgxb201704036
[9] 李伟霖, 王成良, 文俊浩. 基于评论与评分的协同过滤算法[J]. 计算机应用研究, 2017, 34(2): 361-364, 412.
doi: 10.3969/j.issn.1001-3695.2017.02.009
[9] (Li Weilin, Wang Chengliang, Wen Junhao.Collaborative Filtering Recommendation Algorithm Based on Reviews and Ratings[J]. Application Research of Computers, 2017, 34(2): 361-364, 412.)
doi: 10.3969/j.issn.1001-3695.2017.02.009
[10] 陈海涛, 宋姗姗, 李同强. 基于用户的改进的协同过滤推荐算法[J]. 情报理论与实践, 2015, 38(9): 100-103, 133.
doi: 10.16353/j.cnki.1000-7490.2015.09.020
[10] (Chen Haitao, Song Shanshan, Li Tongqiang.Improved User-based Collaborative Filtering Recommendation Algorithm[J]. Information Studies: Theory & Application, 2015, 38(9): 100-103, 133.)
doi: 10.16353/j.cnki.1000-7490.2015.09.020
[11] 吴飞, 余腊生, 冯梅. 基于时间效应的协同过滤算法[J]. 计算机工程与科学, 2017, 39(11): 2095-2101.
[11] (Wu Fei, Yu Lasheng, Feng Mei.A Collaborative Filtering Algorithm Based on Time Effect[J]. Computer Engineering and Science, 2017, 39(11): 2095-2101.)
[12] 兰艳, 曹芳芳. 面向电影推荐的时间加权协同过滤算法的研究[J]. 计算机科学, 2017, 44(4):295-301, 322.
[12] (Lan Yan, Cao Fangfang.Research of Time Weighted Collaborative Filtering Algorithm in Movie Recommendation[J]. Computer Science, 2017, 44(4): 295-301, 322.)
[13] 杨立, 胡运红, 邵桂荣. 融合时间衰减与偏好波动的协同偏好获取方法[J]. 计算机应用, 2016, 36(7): 2011-2015.
doi: 10.11772/j.issn.1001-9081.2016.07.2011
[13] (Yang Li, Hu Yunhong, Shao Guirong.Preference Prediction Method Based on Time Attenuation and Preference Fluctuation[J]. Journal of Computer Applications, 2016, 36(7): 2011-2015.)
doi: 10.11772/j.issn.1001-9081.2016.07.2011
[14] 曾安, 高成思, 徐小强. 融合时间因素和用户评分特性的协同过滤算法[J]. 计算机科学, 2017, 44(9): 243-249.
doi: 10.11896/j.issn.1002-137X.2017.09.046
[14] (Zeng An, Gao Chengsi, Xu Xiaoqiang.Collaborative Filtering Algorithm Incorporating Time Factor and User Preference Properties[J].Computer Science, 2017, 44(9): 243-249.)
doi: 10.11896/j.issn.1002-137X.2017.09.046
[15] 杨锡慧, 林鹏, 周国强. 基于灰色关联度聚类的协同过滤推荐算法[J]. 软件导刊, 2015, 14(10):29-34.
doi: 10.11907/rjdk.151664
[15] (Yang Xihui, Lin Peng, Zhou Guoqiang.Collaborative Filtering Recommendation Algorithm Based on Gray Relational Degree Clustering[J].Software Guide, 2015, 14(10): 29-34.)
doi: 10.11907/rjdk.151664
[16] 邱桂, 闫仁武. 基于灰色关联分析的分布式协同过滤推荐算法[J]. 计算机应用, 2016, 36(4): 1054-1059.
doi: 10.11772/j.issn.1001-9081.2016.04.1054
[16] (Qiu Gui, Yan Renwu.Distributed Collaborative Filtering Recommendation Algorithm Based on Gray Association Analysis[J]. Journal of Computer Applications, 2016, 36(4): 1054-1059.)
doi: 10.11772/j.issn.1001-9081.2016.04.1054
[17] 赵宏晨, 翟丽丽, 张树臣. 基于灰色关联度聚类与标签重叠因子结合的协同过滤推荐方法研究[J]. 计算机工程与科学, 2016, 38(1): 171-176.
[17] (Zhao Hongchen, Zhai Lili, Zhang Shuchen.A Collaborative Filtering Recommendation Method Based on Clustering of Gray Association Degree and Factors of Tag Overlap[J]. Computer Engineering and Science, 2016, 38(1): 171-176.)
[18] 田民, 刘思峰, 卜志坤. 灰色关联度算法模型的研究综述[J]. 统计与决策, 2008(1): 24-27.
[18] (Tian Min, Liu Sifeng, Bu Zhikun.Summary of Gray Correlation Algorithm Model[J]. Statistics & Decision, 2008(1): 24-27.)
[19] 马宏伟, 张光卫, 李鹏. 协同过滤推荐算法综述[J]. 小型微型计算机系统, 2009, 30(7): 1282-1288.
[19] (Ma Hongwei, Zhang Guangwei, Li Peng.Survey of Collaborative Filtering Algorithms[J]. Journal of Chinese Computer Systems, 2009, 30(7): 1282-1288.)
[20] 王茜, 杨莉云, 杨德礼. 面向用户偏好的属性值评分分布协同过滤算法[J]. 系统工程学报, 2010, 25(4): 561-568.
[20] (Wang Qian, Yang Liyun, Yang Deli.Collaborative Filtering Algorithm Based on Rating Distribution of Attributes Faced User Preference[J]. Journal of Systems Engineering, 2010, 25(4): 561-568.)
[21] 朱国玮, 周利. 基于遗忘函数和领域最近邻的混合推荐研究[J]. 管理科学学报, 2012, 15(5): 55-64.
doi: 10.3969/j.issn.1007-9807.2012.05.006
[21] (Zhu Guowei, Zhou Li.Hybrid Recommendation Based on Forgetting Curve and Domain Nearest Neighbor[J]. Journal of Management Sciences in China, 2012, 15(5): 55-64.)
doi: 10.3969/j.issn.1007-9807.2012.05.006
[22] Herlocker J, Konstan J A, Riedl J.An Empirical Analysis of Design Choices in Neighborhood-based Collaborative Filtering Algorithms[J]. Information Retrieval, 2002, 5(4): 287-310.
doi: 10.1023/A:1020443909834
[1] Yang Heng,Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan. Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[2] Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei,Huang Tiankuan. Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[3] Zheng Songyin,Tan Guoxin,Shi Zhongchao. Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts[J]. 数据分析与知识发现, 2020, 4(5): 92-104.
[4] Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
[5] Shan Li,Yehui Yao,Hao Li,Jie Liu,Karmapemo. ISA Biclustering Algorithm for Group Recommendation[J]. 数据分析与知识发现, 2019, 3(8): 77-87.
[6] Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[7] Wang Yong,Wang Yongdong,Guo Huifang,Zhou Yumin. Measuring Item Similarity Based on Increment of Diversity[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
[8] Hua Lingfeng,Yang Gaoming,Wang Xiujun. Recommending Diversified News Based on User’s Locations[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[9] Xue Fuliang,Liu Junling. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[10] Qin Xingxin,Wang Rongbo,Huang Xiaoxi,Chen Zhiqun. Slope One Collaborative Filtering Algorithm Based on Multi-Weights[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
[11] Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time[J]. 现代图书情报技术, 2016, 32(9): 65-69.
[12] Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
[13] Wang Yong,Deng Jiangzhou,Deng Yongheng,Zhang Pu. A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution[J]. 现代图书情报技术, 2016, 32(6): 73-79.
[14] Ma Li. Collaborative Filtering Recommendation Method Based on User Learning Tree[J]. 现代图书情报技术, 2016, 32(4): 72-80.
[15] Shuhao Jiang, Liyi Zhang, Zhixin Zhang. New Collaborative Filtering Algorithm Based on Relative Similarity[J]. 数据分析与知识发现, 2016, 32(12): 44-49.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn