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
  • [21] A Hybrid Recommendation Model for Drug Selection
    Kharma, Qasem M.
    Shambour, Qusai Y.
    Hussein, Abdelrahman H.
    STUDIES IN INFORMATICS AND CONTROL, 2023, 32 (03): : 77 - 87
  • [22] Research of Hybrid Biogeography Based Optimization and Clonal Selection Algorithm for Numerical Optimization
    Qu, Zheng
    Mo, Hongwei
    ADVANCES IN SWARM INTELLIGENCE, PT I, 2011, 6728 : 390 - 399
  • [23] Interactive hybrid recommendation with granule selection
    Zhang, Heng-Ru
    Min, Fan
    Zhang, Ben-Wen
    2014 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING (GRC), 2014, : 362 - 366
  • [24] Hybrid Optimization Method Applied to Adaptive Splitting and Selection Algorithm
    Lopez-Garcia, Pedro
    Wozniak, Michal
    Onieva, Enrique
    Perallos, Asier
    HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, 2016, 9648 : 742 - 750
  • [25] Hybrid Whale Optimization Algorithm with simulated annealing for feature selection
    Mafarja, Majdi M.
    Mirjalili, Seyedali
    NEUROCOMPUTING, 2017, 260 : 302 - 312
  • [26] A new hybrid ant colony optimization algorithm for feature selection
    Kabir, Md. Monirul
    Shahjahan, Md.
    Murase, Kazuyuki
    EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (03) : 3747 - 3763
  • [27] Hybrid particle swarm optimization algorithm for fault feature selection
    Taiyuan University of Technology, Taiyuan 030024, China
    不详
    Xitong Fangzhen Xuebao / Journal of System Simulation, 2008, 20 (15): : 4041 - 4044
  • [28] A Hybrid Rice Optimization Algorithm with Ant System for Feature Selection
    Ye, Zhiwei
    Shu, Zhe
    Liu, Shiuin
    Xia, Xiaoyu
    PROCEEDINGS OF THE 11TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS (IDAACS'2021), VOL 2, 2021, : 700 - 704
  • [29] Hybrid ant colony optimization algorithm for service selection problem
    Zhang, B. (zhangbin@ise.neu.edu.cn), 2013, Northeast University (34):
  • [30] A Hybrid Recommendation Model for Tourist Using Evolutionary Algorithm Combined with Local Search Algorithm for Trip Planning
    J. V. N. Lakshmi
    M. O. Pallavi
    SN Computer Science, 5 (6)
12345