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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, 2020, 46(x): 1−16 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, 2020, 46(x): 1−16 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, 2020, 46(x): 1−16 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, 2020, 46(x): 1−16 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, 博士生导师. 主要研究方向为决策支持. E-mail: caowanhua@vip.163.com

    通讯作者:

    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

Recommendation Methods and Systems Using Knowledge Graph

Funds: Supported by National Natural Science Foundation of China (61403350)
More Information
    Corresponding author: LIU Jun-Tao Senior engineer in Wuhan Digital Engineering Institute. His research interest covers recommender systems, knowledge computing and decision support. Corresponding author of this paper
  • 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

    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1M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 814919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和乙烯基, 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 482M用户数据, 9.14M物品数据, 7952M点击数据, 144M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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