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应用知识图谱的推荐方法与系统

饶子昀 张毅 刘俊涛 曹万华

饶子昀, 张毅, 刘俊涛, 曹万华. 应用知识图谱的推荐方法与系统. 自动化学报, 2021, 47(9): 2061−2077 doi: 10.16383/j.aas.c200128
引用本文: 饶子昀, 张毅, 刘俊涛, 曹万华. 应用知识图谱的推荐方法与系统. 自动化学报, 2021, 47(9): 2061−2077 doi: 10.16383/j.aas.c200128
Rao Zi-Yun, Zhang Yi, Liu Jun-Tao, Cao Wan-Hua. Recommendation methods and systems using knowledge graph. Acta Automatica Sinica, 2021, 47(9): 2061−2077 doi: 10.16383/j.aas.c200128
Citation: Rao Zi-Yun, Zhang Yi, Liu Jun-Tao, Cao Wan-Hua. Recommendation methods and systems using knowledge graph. Acta Automatica Sinica, 2021, 47(9): 2061−2077 doi: 10.16383/j.aas.c200128

应用知识图谱的推荐方法与系统

doi: 10.16383/j.aas.c200128
基金项目: 国家自然科学基金(61403350)资助
详细信息
    作者简介:

    饶子昀:武汉数字工程研究所硕士研究生. 主要研究方向为知识图谱, 推荐系统. E-mail: rzy181234@163.com

    张毅:武汉数字工程研究所高级工程师. 主要研究方向为知识计算, 知识图谱, 数据库技术. E-mail: yzhang85@hrbeu.edu.cn

    刘俊涛:武汉数字工程研究所高级工程师. 主要研究方向为推荐系统, 知识计算, 决策支持. 本文通信作者. E-mail: prolay@163.com

    曹万华:武汉数字工程研究所副所长, 研究员. 主要研究方向为决策支持. E-mail: caowanhua@vip.163.com

Recommendation Methods and Systems Using Knowledge Graph

Funds: Supported by National Natural Science Foundation of China (61403350)
More Information
    Author Bio:

    RAO Zi-Yun Master student at Wuhan Digital Engineering Institute. Her research interest covers knowledge graph and recommendation system

    ZHANG Yi Senior engineer at Wuhan Digital Engineering Institute. His research interest covers knowledge computing, knowledge graph, and database technology

    LIU Jun-Tao Senior engineer at Wuhan Digital Engineering Institute. His research interest covers recommender systems, knowledge computing, and decision support. Corresponding author of this paper

    CAO Wan-Hua Deputy director, proffesor at Wuhan Digital Engineering Institute. His main research interest is decision support

  • 摘要: 数据稀疏和冷启动是当前推荐系统面临的两大挑战. 以知识图谱为表现形式的附加信息能够在某种程度上缓解数据稀疏和冷启动带来的负面影响, 进而提高推荐的准确度. 本文综述了最近提出的应用知识图谱的推荐方法和系统, 并依据知识图谱来源与构建方法、推荐系统利用知识图谱的方式, 提出了应用知识图谱的推荐方法和系统的分类框架, 进一步分析了本领域的研究难点. 本文还给出了文献中常用的数据集. 最后讨论了未来有价值的研究方向.
  • 图  1  推荐系统通用架构

    Fig.  1  General architecture of recommendation system

    图  2  应用知识图谱的推荐系统分类树形图

    Fig.  2  Classification tree diagram of recommendation system using knowledge graph

    图  3  应用知识图谱的推荐系统框架流程

    Fig.  3  Framework flow of recommendation system using knowledge graph

    表  1  主要推荐系统数据集信息

    Table  1  Main recommendation system datasets information

    数据集类别内容在本文综述的文献
    中应用次数
    MovieLens-1M 电影 包含 6000 个用户对 4000 部电影上的 1 M 个评价 9
    MovieLens-20M 电影 包含 138493 个用户对 27278 部电影的 20000263 个评价 3
    Book-Crossings 书籍 90000 个用户, 270000 本书, 1100000 个评分, 评分范围从 1 到 10 5
    Last.FM 音乐 用户 992, 音乐播放记录 19150868 , 对于每个用户, 包含他们最喜欢的艺术家的列表以及播放次数 5
    Yelp 商业点评 4700000 条用户评价, 150000 条商户信息, 200000 张图片, 12 个大都市, 1200000 条商家属性, 随着时间推移在每家商户签到的总用户数 3
    Bing News 新闻 2016 年 10 月 16 日至 2017 年 8 月 11 日从 Bing News8 的服务器日志中收集的
    1025192 条隐式反馈和每条新闻的标题和摘要
    3
    Drug interactions 医学 印第安那大学医学院提供, 药物相互作用表 1
    IntentBooks[60] 书籍 从 Microsoft 的 Bing 搜索引擎和 Microsoft 的 Satori 知识库中收集 1
    ICD-9 ontology 医学 13000 条国际诊断标准代码以及它们之间的关系 1
    Freesound[61] 音乐 3275092 用户, 183246 声音, 48636182 下载记录 1
    MIMIC-III 医学 46520 名患者, 650987 个患者诊断, 1517702 张处方记录(与 6985 种不同疾病和
    4525 种药物相关)
    1
    CEM 旅游 814 919 位单人旅行者, 4800000 笔预订 1
    Amazon-book 书籍 来自 Amazon Review, 65125 用户, 69975 书籍, 828560 用户交互 1
    Amazon 购物 数据集包括四个类别: CD, 服装, 手机和美容 1
    e-commerce
    datasets
    collection
    All Music Guide 音乐 3000000 专辑信息, 自 1991 年以来专家评论数据 1
    Alibaba Taobao 购物 482 M 用户数据, 9.14 M 物品数据, 7952 M 点击数据, 144 M 购买数据 1
    MovieLens-100k 电影 包含 943 个用户对 1682 部电影的 100 K 个评价 1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-13
  • 录用日期:  2020-06-11
  • 网络出版日期:  2021-10-13
  • 刊出日期:  2021-10-13

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