Overview of Collaborative Filtering Recommendation Algorithms

被引：3
作者：
Zhang, Zhen [1 ,2 ]
Peng, Taile [2 ]
Shen, Ke [2 ]
机构：
[1] China Univ Min & Technol, Coll Telecommun, Xuzhou, Jiangsu, Peoples R China
[2] Huaibei Normal Univ, Sch Comp Sci & Technol, Huaibei, Peoples R China
来源：
2019 5TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL APPLICATION | 2020年 / 440卷
关键词：
SYSTEMS;
D O I：
10.1088/1755-1315/440/2/022063
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
In this paper, the key technologies of collaborative filtering recommendation algorithm and the bottleneck in its development are summarized, the problems of different technologies are analyzed. Besides, the application prospect of collaborative filtering technology is prospected.
引用
收藏
页数：6
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共 24 条
  • [1] Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
    Adomavicius, G
    Tuzhilin, A
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2005, 17 (06) : 734 - 749
  • [2] Breese J. S., 1998, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), P43
  • [3] A new similarity function for selecting neighbors for each target item in collaborative filtering
    Choi, Keunho
    Suh, Yongmoo
    [J]. KNOWLEDGE-BASED SYSTEMS, 2013, 37 : 146 - 153
  • [4] The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines
    Eppler, MJ
    Mengis, J
    [J]. INFORMATION SOCIETY, 2004, 20 (05) : 325 - 344
  • [5] Ferdousi Z V, 2018, P 12 ACM C REC SYST
  • [6] Modeling Context-Aware Command Recommendation and Acceptance in an IDE
    Gasparic, Marko
    Ricci, Francesco
    [J]. 2015 IEEE/ACM 2ND INTERNATIONAL WORKSHOP ON CONTEXT FOR SOFTWARE DEVELOPMENT, 2015, : 1 - 5
  • [7] Social network data to alleviate cold-start in recommender system: A systematic review
    Gonzalez Camacho, Lesly Alejandra
    Alves-Souza, Solange Nice
    [J]. INFORMATION PROCESSING & MANAGEMENT, 2018, 54 (04) : 529 - 544
  • [8] Factored similarity models with social trust for top-N item recommendation
    Guo, Guibing
    Zhang, Jie
    Zhu, Feida
    Wang, Xingwei
    [J]. KNOWLEDGE-BASED SYSTEMS, 2017, 122 : 17 - 25
  • [9] A Novel Recommendation Model Regularized with User Trust and Item Ratings
    Guo, Guibing
    Zhang, Jie
    Yorke-Smith, Neil
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2016, 28 (07) : 1607 - 1620
  • [10] Guo GB, 2015, AAAI CONF ARTIF INTE, P123
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