|Table of Contents|

Dynamic Collaborative Filtering Algorithm FusingItem Clustering and Time Weight(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2017年03期
Page:
63-
Research Field:
计算机工程
Publishing date:

Info

Title:
Dynamic Collaborative Filtering Algorithm FusingItem Clustering and Time Weight
Author(s):
Wu Jiawei1; Shen Lingling1; 2; Qian Gang1
(1.School of Computer Science and Technology,Nanjing Normal University,Nanjing 210097,China)(2.School of Business,Nanjing Normal University,Nanjing 210097,China)
Keywords:
collaborative filtering; recommender system; class preference; similarity; time weight
PACS:
P208
DOI:
10.3969/j.issn.1672-1292.2017.03.010
Abstract:
The traditional item-based collaborative filtering algorithm calculates item-item similarity offline and improves the real-time performance of recommender system,but the big data sparsity problem still impacts the quality of the algorithm and it also ignores the phenomenon that users’ interests change over time. To address the issues above,this paper proposes a dynamic collaborative filtering algorithm fusing item clustering and time weight. The proposed algorithm first clusters items according to the user’s preference,then finds out candidate neighbors who are similar to the target item in class preference. Then it searches for nearest neighbors in the candidate neighbor set,which eliminates the interference of the items those have few co-ratings with the target item. At the same time,this algorithm introduces time weight to reflect the change of users’ interests over time,which improves recommendation quality from the overall. Experimental results based on MovieLens dataset show that the recommendation quality of the new algorithm is significantly improved compared with traditional item-based collaborative filtering algorithm and user-based collaborative filtering algorithm.

References:

[1] AL-SHAMRI M Y H. Power coefficient as a similarity measure for memory-based collaborative recommender systems[J]. Expert systems with applications,2014,41(13):5 680-5 688.
[2]CHOI K,SUH Y. A new similarity function for selecting neighbors for each target item in collaborative filtering[J]. Knowledge-based systems,2013,37(1):146-153.
[3]邓爱林,朱扬勇,施伯乐. 基于项目评分预测的协同过滤推荐算法[J]. 软件学报,2003,14(9):1 621-1 628.
DENG A L,ZHU Y Y,SHI B L. A collaborative filtering recommendation algorithm based on item rating prediction[J]. Journal of software,2003,14(9):1 621-1 628.(in Chinese)
[4]罗奇,余英,赵呈领,等. 自适应推荐算法在电子超市个性化服务系统中的应用研究[J]. 通信学报,2006,27(11):183-186,192.
LUO Q,YU Y,ZHAO C L,et al. Research on personalized service system in E-supermarket by using adaptive recommendation algorithm[J]. Journal on communications,2006,27(11):183-186,192.(in Chinese)
[5]SUGANESHWARI G,IBRAHIM S P S. A survey on collaborative filtering based recommendation system[C]//Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges(ISBCC-16’). Springer International Publishing,2016.
[6]NAGARAJU S,KASHYAP M,BHATTACHRAYA M. An effective density based approach to detect complex data clusters using notion of neighborhood difference[J]. International journal of automation and computing,2017,14(1):1-11.
[7]OMAR R,HIROTAKA O,SHIGEMI K. The robustest clusters in the input-output networks:global(\hbox{CO}_2\)emission clusters[J]. Journal of economic structures,2017,6(1):3.
[8]黄典. 基于项目的协同过滤推荐算法的改进[J]. 中国科技信息,2016(1):64-66.
HUANG D. Improvement of item-based collaborative filtering recommendation algorithm[J]. China science and technology information,2016(1):64-66.(in Chinese)
[9]ZHANG Y,LIU Y. A collaborative filtering algorithm based on time period partition[C]//Third International Symposium on Intelligent Information Technology and Security Informatics. New York:IEEE Computer Society,2010:777-780.
[10]田伟,彭玉青. 基于电子商务应用的协同过滤技术改进综述[J]. 计算机工程与科学,2008,30(10):61-63,66.
TIAN W,PENG Y Q. Improvement research of the CF algorithm for E-commerce[J]. Computer engineering and science,2008,30(10):61-63,66.(in Chinese)
[11]陆诗琴. 个性化推荐技术中的互信息相似度应用研究[D]. 桂林:桂林理工大学,2015.
LU S Q. Research on the application of mutual information similarity in personalized recommendation technology[D]. Guilin:Guilin University of Technology,2015.(in Chinese)
[12]RESNICK P,IACOVOU N,SUCHAK M,et al. Group Lens:an open architecture for collaborative filtering of net news[C]//ACM Conference on Computer Supported Cooperative Work. ACM,1994:175-186.
[13]朱文奇. 推荐系统用户相似度计算方法研究[D]. 重庆:重庆大学,2014.
ZHU W Q. Research on user’s similarity calculation method[D]. Chongqing:Chongqing University,2014.(in Chinese)
[14]KIM B M,LI Q,CHANG S P. A new approach for combining content-based and collaborative filters[J]. Journal of intelligent information system,2006,27(1):79-91.
[15]YANG X,GUO Y,LIU Y. A survey of collaborative filtering based social recommender systems[J]. Computer communications,2014,41(5):1-10.
[16]梁昌勇,冷亚军,王勇胜,等. 电子商务推荐系统中群体用户推荐问题研究[J]. 中国管理科学,2013,21(3):153-158.
LIANG C Y,LENG Y J,WANG Y S,et al. Research on group recommendation in e-commerce recommender systems[J]. Chinese journal of management science,2013,21(3):153-158.(in chinese)
[17]魏强,金芝,许焱. 基于概率主题模型的物联网服务发现[J]. 软件学报,2014(8):1 640-1 658.
WEI Q,JIN Z,XU Y. Service discovery for internet of things based on probabilistic topic model[J]. Journal of software,2014(8):1 640-1 658.(in Chinese)
[18]ZHAO Z D,SHANG M S. User-based collaborative-filtering recommendation algorithms on Hadoop[C]//Third international conference on knowledge discovery and data mining. New York:IEEE Computer Society,2010:478-481.

Memo

Memo:
-
Last Update: 2017-09-30