A collaborative filtering recommendation method to the loyal-user problem

被引:0
作者
Huang Yongsheng [1 ]
Meng Xiangwu [1 ]
Zhang Yujie [1 ]
机构
[1] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100088, Peoples R China
来源
2009 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, VOL 2 | 2009年
关键词
recommender system; collaborative filterinng; loyal user; data sparsity; INFORMATION;
D O I
10.1109/ICCSIT.2009.5234854
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Although a variety of recommendation techniques have been proposed in recent years, collaborative filtering (CF) has been considered to be the most successful recommendation technique. Data sparsity is one of the problems which attract most attention from researchers. Data sparsity means there are lots of users having no or a small amount of rated item. Prediction to rating score of user's unrated items is imprecision. In contrast to the data sparsity problem, what is called as loyal-user problem in this paper is neglected. In this paper, the loyal-user problem is analyzed and an approach is proposed to solve the problem. The experimental results also demonstrate the effectiveness of the method.
引用
收藏
页码:57 / 60
页数:4
相关论文
共 50 条
  • [1] Collaborative filtering recommendation algorithm based on user fuzzy similarity
    Wu, Yitao
    Zhang, Xingming
    Yu, Hong
    Wei, Shuai
    Guo, Wei
    INTELLIGENT DATA ANALYSIS, 2017, 21 (02) : 311 - 327
  • [2] Collaborative Filtering Recommendation Based on Item Quality and User Ratings
    Jiao F.
    Li S.
    Data Analysis and Knowledge Discovery, 2019, 3 (08): : 62 - 67
  • [3] Collaborative filtering recommendation based on dynamic changes of user interest
    Gasmi, Ibtissem
    Seridi-Bouchelaghem, Hassina
    Hocine, Labar
    Abdelkarim, Baareh
    INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, 2015, 9 (03): : 271 - 281
  • [4] A User Trust-Based Collaborative Filtering Recommendation Algorithm
    Zhang, Fuzhi
    Bai, Long
    Gao, Feng
    INFORMATION AND COMMUNICATIONS SECURITY, PROCEEDINGS, 2009, 5927 : 411 - 424
  • [5] Collaborative Filtering Recommendation Algorithm Based on User Acceptable Rating Radius
    Huang, Yue
    Gao, Xuedong
    Gu, Shujuan
    LISS 2013, 2015, : 141 - 146
  • [6] A collaborative filtering recommendation algorithm based on user preferences on service properties
    Mu, Wenzhong
    Meng, Fanchao
    Chu, Dianhui
    PROCEEDINGS 2014 INTERNATIONAL CONFERENCE ON SERVICE SCIENCES (ICSS 2014), 2014, : 43 - 46
  • [7] Joining User Clustering and Item Based Collaborative Filtering in Personalized Recommendation Services
    Gong, SongJie
    Ye, HongWu
    2009 INTERNATIONAL CONFERENCE ON INDUSTRIAL AND INFORMATION SYSTEMS, PROCEEDINGS, 2009, : 149 - +
  • [8] Collaborative filtering recommendation algorithm based on user interest characteristics and item category
    Zhang, L. (zhangls@cqupt.edu.cn), 1600, Binary Information Press, P.O. Box 162, Bethel, CT 06801-0162, United States (09): : 5973 - 5986
  • [9] Contextual Collaborative Filtering Recommendation Model Integrated with Drift Characteristics of User Interest
    Guo, Feipeng
    Lu, Qibei
    HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES, 2021, 11
  • [10] A Collaborative Filtering Recommendation Algorithm Based on Biclustering
    Wang, Jiasheng
    Song, Hong
    Zhou, Xiaofeng
    2015 IEEE INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER), 2015, : 803 - 807