A hybrid of local optimization selection for recommendation algorithm

被引：0
|
作者：
Liu, Huiting [1 ]
Chen, Chao [1 ]
Wu, Gongqing [2 ]
Zhao, Peng [1 ]
机构：
[1] School of Computer Science and Technology, Anhui University, Hefei , China
[2] School of Computer Science and Information Engineering, Hefei University of Technology, Hefei , China
来源：
Journal of Information and Computational Science | 2014年 / 11卷 / 18期
关键词：
To overcome the impact on data sparsity of traditional collaborative filtering; we present a hybrid of local optimization selection for recommendation algorithm; named CCFHU. We firstly combine the similarity of feature attributes with the rating of items; to improve the computation result of the similarity of items. Then; we use local optimization options to select neighbors as a reference of the target group. Finally; we propose the content-based hybrid recommendation algorithm to amend abnormal predicted results in the collaborative filtering method. Our experimental results show that the CCFHU algorithm can reduce the negative impact of data sparsity on recommendations; improve the calculation accuracy of similarity between items; and effectively lower the mean absolute error of prediction consequences. Our experiments further contrast CCFHU with the content-boosted collaborative filtering and linear weighted method of collaborative filtering based on content prediction. It turns out that in terms of prediction accuracy; the CCFHU algorithm can achieve better results;
D O I：
10.12733/jics20105052
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：6813 / 6824
相关论文
共 50 条
  • [1] The optimization of weights in weighted hybrid recommendation algorithm
    Lin, Wenfu
    Li, Ying
    Feng, Shuang
    Wang, Yongbin
    2014 IEEE/ACIS 13TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS), 2014, : 405 - 408
  • [2] Hybrid Global Optimization Algorithm for Feature Selection
    Azar, Ahmad Taher
    Khan, Zafar Iqbal
    Amin, Syed Umar
    Fouad, Khaled M.
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 74 (01): : 2021 - 2037
  • [3] Recommendation using a clustering algorithm based on a hybrid features selection method
    Hdioud Ferdaous
    Frikh Bouchra
    Ouhbi Brahim
    Mountasser Imad-eddine
    Benghabrit Asmaa
    Journal of Intelligent Information Systems, 2018, 51 : 183 - 205
  • [4] Recommendation using a clustering algorithm based on a hybrid features selection method
    Ferdaous, Hdioud
    Bouchra, Frikh
    Brahim, Ouhbi
    Imad-eddine, Mountasser
    Asmaa, Benghabrit
    JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2018, 51 (01) : 183 - 205
  • [5] Hybrid recommendation algorithm based on improved discrete particle Swarm optimization
    Wang, Tong
    Qu, Guixue
    ICIC Express Letters, 2014, 8 (09): : 2625 - 2630
  • [6] Parameters optimization of hybrid strategy recommendation based on particle swarm algorithm
    Cai, Biao
    Zhu, Xinping
    Qin, Yangxin
    EXPERT SYSTEMS WITH APPLICATIONS, 2021, 168
  • [7] A hybrid service selection optimization algorithm in internet of things
    Zhang, Xiaofei
    Geng, Juncheng
    Ma, Jianwei
    Liu, Hao
    Niu, Shuangxia
    Mao, Wandeng
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2021, 2021 (01)
  • [8] A New Hybrid Seagull Optimization Algorithm for Feature Selection
    Jia, Heming
    Xing, Zhikai
    Song, Wenlong
    IEEE ACCESS, 2019, 7 : 49614 - 49631
  • [9] A hybrid service selection optimization algorithm in internet of things
    Xiaofei Zhang
    Juncheng Geng
    Jianwei Ma
    Hao Liu
    Shuangxia Niu
    Wandeng Mao
    EURASIP Journal on Wireless Communications and Networking, 2021
  • [10] A hybrid genetic quantitative algorithm for portfolio selection optimization
    Liu, ZD
    Proceedings of the 2005 International Conference on Management Science & Engineering (12th), Vols 1- 3, 2005, : 1834 - 1838
12345