Clique discovery based on user similarity for online shopping recommendation

被引:1
|
作者
Yang Q. [1 ]
Zhou P. [1 ]
Zhang H. [2 ]
Zhang J. [2 ]
机构
[1] Electronic Engineering and Automation Institute, Guilin University of Electronic Technology, Guilin
[2] Computer and Control Institute, Guilin University of Electronic Technology, Guilin
关键词
Clique core; Clique discovery; Clique leader; Online recommendation;
D O I
10.3923/itj.2011.1587.1593
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Identifying cliques with the same interests is valuable for online shopping which can make the recommendation and advertisements to target different users more accurately and maximize the benefits of advertisers, publishers and users. This study, has proposed an effective and efficient method to discover cliques for online shopping which firstly identifies clique leaders and clusters the most similar users, then computes clique cores among existing clique members and finally generates the complete cliques. A marked improvement is that two key factors, users' behavioral characteristics and regular purchase information, are unified to discover cliques. This method can also remove effectively most of fake purchases through computing the operation similarity among different goods categories. © 2011 Asian Network for Scientific Information.
引用
收藏
页码:1587 / 1593
页数:6
相关论文
共 50 条
  • [21] An improved collaborative recommendation algorithm based on optimized user similarity
    Hao Chen
    Zhongkun Li
    Wei Hu
    The Journal of Supercomputing, 2016, 72 : 2565 - 2578
  • [22] Multi-user recommendation algorithm based on vulnerability similarity
    Jia F.
    Kang S.
    Jiang W.
    Wang G.
    Qinghua Daxue Xuebao/Journal of Tsinghua University, 2023, 63 (09): : 1399 - 1407
  • [23] Cross-Domain Item Recommendation Based on User Similarity
    Xu, Zhenzhen
    Jiang, Huizhen
    Kong, Xiangjie
    Kang, Jialiang
    Wang, Wei
    Xia, Feng
    COMPUTER SCIENCE AND INFORMATION SYSTEMS, 2016, 13 (02) : 359 - 373
  • [24] Recommendation Systems Based on Online User's Action
    Elkhelifi, Aymen
    Ben Kharrat, Firas
    Faiz, Rim
    CIT/IUCC/DASC/PICOM 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY - UBIQUITOUS COMPUTING AND COMMUNICATIONS - DEPENDABLE, AUTONOMIC AND SECURE COMPUTING - PERVASIVE INTELLIGENCE AND COMPUTING, 2015, : 485 - 490
  • [25] A personalized recommendation model for online apparel shopping based on Kansei engineering
    Zhou, Xiaoxi
    Liang, Hui'e
    Dong, Zhiya
    INTERNATIONAL JOURNAL OF CLOTHING SCIENCE AND TECHNOLOGY, 2017, 29 (01) : 2 - 13
  • [26] Online recommendation based on customer shopping model in e-commerce
    Ji, JZ
    Sha, ZQ
    Liu, CN
    Zhong, N
    IEEE/WIC INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, PROCEEDINGS, 2003, : 68 - 74
  • [27] Community Discovery Algorithm Based on User Behavior Similarity
    Wang, Hao
    Pan, Lilian
    Dong, Zheng
    Wang, Wen
    Li, DanDan
    Duan, JianYong
    PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019), 2019, : 1160 - 1165
  • [28] E-Commerce Online Shopping Platform Recommendation Model Based on Integrated Personalized Recommendation
    Xu, Lijuan
    Sang, Xiaokun
    SCIENTIFIC PROGRAMMING, 2022, 2022
  • [29] CSD: A multi-user similarity metric for community recommendation in online social networks
    Han, Xiao
    Wang, Leye
    Farahbakhsh, Reza
    Cuevas, Angel
    Cuevas, Ruben
    Crespi, Noel
    He, Lina
    EXPERT SYSTEMS WITH APPLICATIONS, 2016, 53 : 14 - 26
  • [30] Individualized Garment Recommendation System for Online Shopping
    Sekozawa, Teruji
    PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON SYSTEM SCIENCE AND SIMULATION IN ENGINEERING (ICOSSSE '09), 2009, : 51 - 56