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基于深度学习的混合兴趣点推荐算法

冯浩 黄坤 李晶 高榕 刘东华 宋成芳

赵斌, 王刚, 王东蕾. LCLC谐振变换器谐振电流的研究[J]. 电子与信息学报, 2017, 39(6): 1479-1486. doi: 10.11999/JEIT160752
引用本文: 冯浩, 黄坤, 李晶, 高榕, 刘东华, 宋成芳. 基于深度学习的混合兴趣点推荐算法[J]. 电子与信息学报, 2019, 41(4): 880-887. doi: 10.11999/JEIT180458
ZHAO Bin, WANG Gang, WANG Donglei. Research on the Resonant Current of the LCLC Resonant Converters[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1479-1486. doi: 10.11999/JEIT160752
Citation: Hao FENG, Kun HUANG, Jing LI, Rong GAO, Donghua LIU, Chengfang SONG. Hybrid Point of Interest Recommendation Algorithm Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2019, 41(4): 880-887. doi: 10.11999/JEIT180458

基于深度学习的混合兴趣点推荐算法

doi: 10.11999/JEIT180458
基金项目: 国家自然科学基金(41201404),中央高校基本科研业务费专项资金(2042015gf0009)
详细信息
    作者简介:

    冯浩:男,1979年生,博士,高级工程师,研究方向为体系结构和系统工程

    黄坤:男,1979年生,博士,高级工程师,研究方向为人工智能和系统工程

    李晶:男,1967年生,博士,教授,研究方向为数据挖掘和多媒体技术

    高榕:男,1981年生,博士,研究方向为数据挖掘和智能推荐

    刘东华:女,1989年生,博士生,研究方向为数据挖掘和智能推荐

    宋成芳:男,1978年生,博士,讲师,研究方向为可视化分析和位置服务

    通讯作者:

    李晶 leejingcn@163.com

  • 中图分类号: TP311

Hybrid Point of Interest Recommendation Algorithm Based on Deep Learning

Funds: The National Natural Science Foundation of China (41201404), The Fundamental Research Funds for the Central Universities of China (2042015gf0009)
  • 摘要:

    针对现有兴趣点推荐的初始化和忽视评论信息语义上下文信息的问题,将深度学习融入推荐系统中已经成为兴趣点推荐研究的热点之一。该文提出一种基于深度学习的混合兴趣点推荐模型(MFM-HNN)。该模型基于神经网络融合评论信息与用户签到信息来提高兴趣点推荐的性能。具体地,利用卷积神经网络学习评论信息的特征表示,利用降噪自动编码对用户签到信息进行初始化。进而,基于扩展的矩阵分解模型融合评论信息特征和用户签到信息的初始值进行兴趣点推荐。在真实签到数据集上进行实验,结果表明所提MFM-HNN模型相比其他先进的兴趣点推荐具有更好的推荐性能。

  • 图  1  基于混合神经网络矩阵分解的兴趣点推荐模型

    图  2  MFM-HNN模型基于LA数据集和NYC数据集与其他4个模型的推荐性能对比

    图  3  基于LA和NYC数据集的5个初始化方法的性能对比

    图  4  MFM-HNN模型基于LA数据集和NYC数据集在不同层数的性能对比

    表  1  MFM-HNN模型学习算法

     输入:xi,Su,Sv,˜Su,˜Sv,T,B
     输出:L
     (1) For t<T Do
     (2) 从兴趣点评论中随机选取一个兴趣点的评论矩阵xi进行训练,   训练批次大小为β0,每一个批次的大小为B,计算训练过程   中的损失Lcnn
     (3) if t>T or Lcnn足够小
     (4) end
     (5) for t<T Do
     (6) 从兴趣点评分中随机选取一个兴趣点的用户-兴趣点对(˜sui,˜svi)   进行训练,训练批次大小为β1,每一个批次的大小为B,计算    训练过程中的损失Lui
     (7) if t>T or Lui足够小
     (8) end
     (9) 计算最终的损失值L=Lcnn+Lui
     (10) return L
    下载: 导出CSV

    表  2  数据集统计

    数据统计LANYC
    用户数量30,20847,240
    兴趣点数量142,798203,765
    签到数量(评论)244,861388,954
    用户-位置矩阵密度5.68×10–54.04×10–5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-14
  • 修回日期:  2018-11-26
  • 网络出版日期:  2018-12-05
  • 刊出日期:  2019-04-01

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