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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (12): 1-13    DOI: 10.11925/infotech.2096-3467.2021.0516
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Review of Recommendation Systems Based on Knowledge Graph
Zhu Dongliang,Wen Yi(),Wan Zichen
Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041,China
Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190,China
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Abstract  

[Objective] This paper reviewed the latest achievements of recommendation systems based on the knowledge graph. [Coverage] We used “knowledge graph”, “KG”, “recommendation system”, “RS”, and “recommended system” as key words to search the Web of Science, CNKI, Wanfang and other scholarly databases. A total of 70 documents were reviewed. [Methods] First, we summarized the classification of recommendation algorithms based on knowledge graphs. Then, we sorted the development history of recommendation systems using different types of algorithms. Finally, we discussed the typical algorithms and their future development trends. [Results] The reviewed recommendation systems were based on connection, embedding and hybrid methods. The three types of algorithms have advantages and disadvantages in different scenarios. Maximizing the utilization of graph information and reducing the computing power consumption is the future direction. [Limitations] We did not include the commercial examples of the recommendation systems. [Conclusions] The knowledge graph and machine learning could effectively improve the traditional recommendation algorithms.
Key words： Knowledge Graph      Recommendation System      Data Mining     
Received: 24 May 2021      Published: 20 January 2022
ZTFLH:  TP393  
Fund:Special Project of Chinese Academy of Sciences’ Literature and Information Capacity Building(Y9290002-3.5.3)
Corresponding Authors: Wen Yi,ORCID：0000-0002-6520-2733     E-mail: weny@clas.ac.cn
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Dongliang Zhu
Yi Wen
Zichen Wan

Cite this article:

Zhu Dongliang, Wen Yi, Wan Zichen. Review of Recommendation Systems Based on Knowledge Graph. Data Analysis and Knowledge Discovery, 2021, 5(12): 1-13.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0516     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I12/1

应用领域 应用实例
娱乐活动 音乐 网易云、QQ音乐、酷狗音乐
美食 美团、大众点评、口碑
新闻 今日头条、网易新闻、腾讯新闻、搜狐新闻
视频 Netflix、抖音、快手、腾讯视频、爱奇艺、优酷
旅行 携程、爱彼迎、去哪儿网
日常生活 购物 淘宝、天猫、拼多多、京东
阅读 亚马逊、当当、书旗小说、番茄小说
社交 Facebook、推特、新浪微博
求职 智联招聘、BOSS直聘、实习僧
就医 丁香医生、医学界
生产实践 农业 一亩田、惠农网、我会种
工业 阿里云工业大脑、智慧城市、智慧电网
服务业 安居客、瓜子、世纪佳缘
Some Examples of Recommendation System
Recommendation Example Based on Knowledge Graph
推荐模型 发表年份 核心算法/思想 数据集 基线模型 评价指标
基于连接的推荐 HeteroMF[30] 2013 集体矩阵分解（CMF） Epinions
Flixster		 CMF
SMF		 RMSE
Hete-MF[31] 2013 矩阵分解（MF） IMDb-MovieLens-100K NMF
WNMF		 MAE
RMSE
HeteRec-p[32] 2014 贝叶斯排序（BPR）
元路径传播		 IMDb-MovieLens-100K
Yelp		 NMF
Hybrid-SVM		 Prec@k
MRR
Hete-CF[33] 2014 协同过滤（CF） DBLP
Meetup		 UserMean
ItemMean
NMF		 MAE
RMSE
SemRec[34] 2015 加权元路径相似性度量 豆瓣
Yelp		 PMF
SMF
CMF		 MAE
RMSE
GraphLF[35] 2016 概率逻辑系统（ProPPR） MovieLens-100K
Yelp		 HeteRec-p
NB		 MRR
Precision
FMG[36] 2017 矩阵分解（MF）
主成分分析（PCA）
线性判别分析（LDA）		 Amazon
Yelp		 RegSVD
HeteRec
SemRec		 RMSE
基于嵌入的推荐 Node2Vec[37] 2016 随机梯度下降（SGD）
Skip-gram
离散傅立叶变换（DFT）
广度优先抽样（BFS）		 Les-Misérables-network Spectral clustering
DeepWalk
LINE		 Micro-F1
Macro-F1
Entity2Rec[38] 2017 Node2Vec
LambdaMart
Adarank		 MovieLens-1M NMF
SVD
ItemKNN		 Prec@k
MAP
DKN[39] 2018 卷积神经网络（CNN）
深度神经网络（DNN）
注意力机制		 Bing News LibFM
DeepWide
DMF		 F1
AUC
RKGE[40] 2018 循环神经网络（RNN） MovieLens-1M
Yelp		 HeteRS
HeteRec
GraphLF		 Prec@k
MRR
MKR[41] 2019 多层感知器（MLP）
多任务学习（MTL）		 MovieLens-1M
Book-Crossing
Last.FM
Bing-News		 DKN
RippleNet
LibFM
Wide&Deep		 AUC
ACC
Precision
Recall
SI-MKR[42] 2020 多层感知器（MLP）
卷积神经网络（CNN）		 MovieLens-1M DKN
Wide&Deep
MKR		 Precision
Recall
基于混合的推荐 RippleNet[43] 2018 偏好传播 MovieLens-1M
Book-Crossing
Bing-News		 CKE
SHINE
DKN
LibFM
Wide&Deep		 Precision
Recall
F1
AUC
ACC
KGCN[44] 2019 图卷积神经网络（GCN）
注意力机制		 MovieLens-20M
Book-Crossing
Last.FM		 LibFM
LibFM + TransE
RippleNet		 AUC
F1
Recall
KGAT[45] 2019 图卷积神经网络（GCN）
TransR
注意力机制		 Amazon-book
Last-FM
Yelp2018		 NFM
CKE
MCRec
RippleNet		 Recall
NDCG
RecKGC[46] 2019 知识图谱补全（KGC）
注意力机制		 MovieLens
Amazon Music		 CDL
NeuMF
DistMult		 HR@k
NDCG
Hits@10
MRR
GraphRec[47] 2019 图神经网络（GNNs）
多层感知器（MLP）		 Ciao
Epinions		 SoRec
NeuMF
GCMC+SN		 MAE
RMSE
NIA-GCN[48] 2020 逐层邻居聚合（PNA）
并行图卷积网络（Parallel-GCNs）
跨深度集成（CDE）		 Gowalla
Amazon-Electronics
Amazon-CDs
Amazon-Movies		 NeuMF
GC-MC
PinSAGE-LSTM
NGCF		 Recall
NDCG
Logloss
AUC
MBGCN[49] 2020 图卷积神经网络（GCN） Tmall
Beibei		 NeuMF
GraphSAGE-OB
RGCN		 Recall
NDCG
NeuACF[50] 2021 深度神经网络（DNN）
自注意力机制		 MovieLens
Amazon-Electronics		 DMF
HeteRS
FMG		 HR@k
NDCG@k
Recommendation Models Based on Knowledge Graph
Recommendation System Classification Based on Knowledge Graph Embedding
推荐框架 典型模型 优点 缺点
基于连接的推荐 HeteroMF、
Hete-MF、
Hete-CF等		 充分利用知识图谱的连接关系,实现基于连接的预测与推荐 需手动设计元路径,跨领域推荐能力缺失,实际场景应用范围有限
基于嵌入的推荐 Node2Vec、
entity2rec、
DKN、
RKGE等		 引入向量表示,降低知识图谱的高维性和异构性,减轻特征工程工作,提高知识图谱应用灵活度 忽略了知识图谱中的信息连通性,无法利用实体间多跳关系,无法挖掘图谱中高阶关系,部分推荐结果缺乏可解释性
基于混合的推荐 RippleNet、
KGCN、
KGAT 等		 引入向量表示的同时充分利用实体连接关系,挖掘高阶语义,实现精准推荐 复杂模型带来较多资源消耗、多参数调优等难题,在大规模业务数据情况下的扩展性能有限
Comparison of Recommended Ideas Based on Knowledge Graph
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