0、知识图谱入门

浙江大学最新《知识图谱》课程，八堂课全面讲述识图谱的基本概念、核心技术内涵和应用实践方法
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](https://mp.weixin.qq.com/s/lUcuWhBxjDudD_khNX0fMQ)
【浙江大学知识图谱课程】 第一讲-第4节-知识图谱的技术内涵
【浙江大学知识图谱课程】 | 第一讲-第3节-知识图谱的价值
浙大图谱讲义 | 第一讲-知识图谱概论 — 第2节-知识图谱的起源
综述 | 知识图谱技术综述（上）
综述 | 知识图谱技术综述（下）
【知识图谱实战】上分！上分！上分！OGB比赛的4大上分技巧
阿里小蜜多模态知识图谱的构建及应用
知识图谱基本概念以及知识图谱嵌入模型
图谱实战 | 知识图谱构建的一站式平台gBuilder
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](https://mp.weixin.qq.com/s/GZ40cpoO_JLvyoa_TEbQzQ)
【综述系列】干货速看！知识图谱 or 机器学习
【知识图谱系列】六篇2020年知识图谱预训练论文综述
【知识图谱系列】知识图谱+图神经网络
【知识图谱系列】万字长文，图表示学习中的Encoder-Decoder框架
【预训练模型系列II】预训练模型与知识图谱相结合的研究进展
【预训练模型系列III】知识图谱预训练模型的新进展综述 GCC、GraphCL、DGI、InfoGraph、Multi-View

论文小综 | Pre-training on Graphs
【关于 知识图谱】 那些你不知道的事

知识表示技术：图谱表示VS图网络表示及基于距离函数的表示学习总结
赵学敏：京东商品图谱构建与实体对齐
业界分享 | 美团到店综合知识图谱的构建与应用

一、基础

1.1 一般

BPR: Bayesian Personalized Ranking from Implicit Feedback

  • Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, et al. BPR: Bayesian Personalized Ranking from Implicit Feedback[C]. In UAI 2009.

Personalized entity recommendation: a heterogeneous information network approach

  • Xiao Yu, Xiang Ren, Yizhou Sun, et al. Personalized entity recommendation: a heterogeneous information network approach[C]. In WSDM 2014.

Adam: A Method for Stochastic Optimization

  • Diederik P. Kingma, Jimmy Lei Ba. Adam: A Method for Stochastic Optimization[C]. In ICLR 2015.

Learning Disentangled Representations for Recommendation

  • Jianxin Ma, Chang Zhou, Peng Cui, et al. Learning Disentangled Representations for Recommendation[C]. In NeurIPS 2019.

NAIS: Neural Attentive Item Similarity Model for Recommendation

  • Xiangnan He, Zhankui He, Jingkuan Song, et al. NAIS: Neural Attentive Item Similarity Model for Recommendation[J]. In TKDE 2018.

DeepInf: Social Influence Prediction with Deep Learning

  • Jiezhong Qiu, Jian Tang, Hao Ma, et al. DeepInf: Social Influence Prediction with Deep Learning[C]. In KDD 2018.

Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks

  • 基于元图的异构信息网络推荐融合
  • Huan Zhao, Quanming Yao, Jianda Li, et al. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks[C]. In KDD 2017.

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

  • Huifeng Guo, Ruiming Tang, Yunming Ye, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[C]. In IJCAI 2017.

DIN: Deep Interest Network for Click-Through Rate Prediction

  • Guorui Zhou, Chengru Song, Xiaoqiang Zhu, et al. Deep Interest Network for Click-Through Rate Prediction[C]. In KDD 2018.

NFM: Neural Factorization Machines for Sparse Predictive Analytics

  • Xiangnan He, Tat-Seng Chua. Neural Factorization Machines for Sparse Predictive Analytics[C]. In SIGIR 2017.

ACF: Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

  • Jingyuan Chen, Hanwang Zhang, Xiangnan He, et al. Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention[C]. In SIGIR 2017.

NeuralCF: Neural Collaborative Filtering

  • Xiangnan He, Lizi Liao, Hanwang Zhang, et al. Neural Collaborative Filtering[C]. In WWW 2017.

NSCR: Item Silk Road: Recommending Items from Information Domains to Social Users

  • Xiang Wang, Xiangnan He, Liqiang Nie, et al. Item Silk Road: Recommending Items from Information Domains to Social Users[C]. In SIGIR 2017.

其他
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

  • 基于知识图增强强化学习的交互式推荐系统
  • Sijin Zhou, Xinyi Dai, Haokun Chen, et al. Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning[C]. In SIGIR 2020.

Reinforced Negative Sampling over Knowledge Graph for Recommendation

  • 基于知识图谱的强化负抽样推荐方法
  • Xiang Wang, Yaokun Xu, Xiangnan He, et al. Reinforced Negative Sampling over Knowledge Graph for Recommendation[C]. In WWW 2020.

KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation

  • KERL：一种面向序列推荐的知识引导强化学习模型
  • Pengfei Wang, Yu Fan, Long Xia, et al. KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation[C]. In SIGIR 2020.

CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems

Translating Embeddings for Modeling Multi-relational Data

  • Antoine Bordes, Nicolas Usunier, Alberto Garcia-Durán, et al. Translating Embeddings for Modeling Multi-relational Data[C]. In NeurIPS 2013.

Learning Entity and Relation Embeddings for Knowledge Graph Completion

  • Yankai Lin, Zhiyuan Liu, Maosong Sun, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion[C]. In AAAI 2015.

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

  • Zhiqing Sun, _Zhi-Hong Deng, Jian-Yun Nie, et al. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space[C]. In **_ICLR 2019.**

    1.3 图神经网络

    GCN: Semi-Supervised Classification with Graph Convolutional Networks

  • Thomas N. Kipf, Max Welling. Semi-Supervised Classification with Graph Convolutional Networks[C]. In ICLR 2017.

GraphSAGE: Inductive Representation Learning on Large Graphs

  • William L. Hamilton, Rex Ying, Jure Leskovec. Inductive Representation Learning on Large Graphs[C]. In NeurIPS 2017.

GAT: Graph Attention Networks

SGC: Simplifying Graph Convolutional Networks

  • Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr. et al. Simplifying Graph Convolutional Networks[C]. In ICML 2019.

DisenGCN: Disentangled Graph Convolutional Networks

  • Jianxin Ma, Peng Cui, Kun Kuang, et al. Disentangled Graph Convolutional Networks[C]. In ICML 2019.

SuperGAT: How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision

NGCF: Neural Graph Collaborative Filtering

  • Xiang Wang, Xiangnan He, Meng Wang, et al. Neural Graph Collaborative Filtering[C]. In SIGIR 2019.

DGCF: Disentangled Graph Collaborative Filtering

  • Xiang Wang, Hongye Jin, An Zhang, et al. Disentangled Graph Collaborative Filtering[C]. In SIGIR 2020.

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

  • Xiangnan He, Kuan Deng, Xiang Wang, et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation[C]. In SIGIR 2020.

SGL: Self-supervised Graph Learning for Recommendation

  • Jiancan Wu, Xiang Wang, Fuli Feng, et al. Self-supervised Graph Learning for Recommendation[C]. In SIGIR 2021.

NCL: Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

  • Zihan Lin, Changxin Tian, Yupeng Hou, et al. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning[C]. In WWW 2022.

    二、知识图谱推荐

    2.0 综述、总结、进展

    A Survey on Knowledge Graph-Based Recommender Systems

  • Qingyu Guo, Fuzhen Zhuang, Chuan Qin, et al. A Survey on Knowledge Graph-Based Recommender Systems[C]. In TKDE 2020.

  • 基于知识图谱的推荐系统综述

Deep Learning on Knowledge Graph for Recommender System: A Survey

  • Yang Gao, Yi-Fan Li, Yu Lin, et al. Deep Learning on Knowledge Graph for Recommender System: A Survey[J]. In arXiv 2020.

深度融合 | 当推荐系统遇见知识图谱
如何将知识图谱引入推荐系统？
兴趣点图谱用于内容理解的分享。

2.1 知识图谱—-解释、推理

TEM: Tree-enhanced Embedding Model for Explainable Recommendation

  • Xiang Wang, Xiangnan He, Fuli Feng, et al. TEM: Tree-enhanced Embedding Model for Explainable Recommendation[C]. In WWW 2018.

Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs

  • 可解释的推荐、强化学习、知识图谱推理
  • Kangzhi Zhao, Xiting Wang, Yuren Zhang, et al. Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs[C]. In SIGIR 2020.

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

  • 用于解释性推荐的强化知识图谱推理
  • Yikun Xian, Zuohui Fu, S.Muthukrishnan, et al. Reinforcement Knowledge Graph Reasoning for Explainable Recommendation[C]. In SIGIR 2019.

Explainable Reasoning over Knowledge Graphs for Recommendation

  • 基于知识图谱的可解释推理推荐
  • Xiang Wang, Dingxian Wang, Canran Xu, et al. Explainable Reasoning over Knowledge Graphs for Recommendation[C]. In AAAI 2019.

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

  • 学习异构知识库嵌入以实现可解释推荐

  • Qingyao Ai, Vahid Azizi, Xu Chen, et al. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation[J]. In Algorithms 2018.

    2.2 基于嵌入的方法

    Recurrent knowledge graph embedding for effective recommendation

  • 用于有效推荐的递归知识图谱嵌入

  • Zhu Sun, Jie Yang, Jie Zhang, et al. Recurrent knowledge graph embedding for effective recommendation[C]. In RecSys 2018.

CKE: Collaborative Knowledge Base Embedding for Recommender Systems

DKN: Deep Knowledge-Aware Network for News Recommendation

  • DKN：面向新闻推荐的深度知识感知网络
  • Hongwei Wang, Fuzheng Zhang, Xing Xie, et al. DKN: Deep Knowledge-Aware Network for News Recommendation[C]. In WWW 2018.

KSR: Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks

  • 用知识增强的记忆网络改进序列推荐

  • Jin Huang, Wayne Xin Zhao, Hongjian Dou, et al. Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks[C]. In SIGIR 2018.

    2.3 基于路径

    RuleRec: Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

  • 用知识图联合学习推荐的可解释规则

  • Weizhi Ma, Min Zhang, Yue Cao, et al. Jointly Learning Explainable Rules for Recommendation with Knowledge Graph[C]. In WWW 2019.

Leveraging Meta-path based Context for Top-N Recommendation with A Neural Co-Attention Model

  • 利用元路径上下文进行Top-N推荐的神经共注意力模型

  • Binbin Hu, Chuan Shi, Wayne Xin Zhao, et al. Leveraging Meta-path based Context for Top-N Recommendation with A Neural Co-Attention Model[C]. In KDD 2018.

    2.4 联合的方法

    RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

  • Hongwei Wang, Fuzheng Zhang, Jialin Wang, et al. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems[C]. In CIKM 2018.

KGCN: Knowledge Graph Convolutional Networks for Recommender Systems

KGAT: Knowledge Graph Attention Network for Recommendation

Are we really making much progress Revisiting, benchmarking, and refining heterogeneous graph neural networks

KGNN-LS: Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

  • Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, et al. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems[C]. In KDD 2019.

IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation

  • Jun Zhao, Zhou Zhou, Ziyu Guan, et al. IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation[C]. In KDD 2019.

AKGE: Attentive Knowledge Graph Embedding for Personalized Recommendation

  • Xiao Sha, Zhu Sun, Jie Zhang, et al. Attentive Knowledge Graph Embedding for Personalized Recommendation[J]. In Electron. Commer. Res. Appl. 2019.

KNI: An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation

  • Yanru Qu, Ting Bai, Weinan Zhang, et al. An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation[C]. In DLP-KDD 2019.

KTUP: Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

  • Yixin Cao, Xiang Wang, Xiangnan He, et al. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences[C]. In WWW 2019.

CKAN：Collaborative Knowledge-aware Attentive Network for Recommender Systems

MVIN: Learning Multiview Item for Recommendation

KGIN: Learning Intents behind Interactions with Knowledge Graph for Recommendation

AKGAN: Knowledge graph enhanced recommender system 2021.12.17

  • Zepeng Huai, Jianhua Tao, Feihu Che, et al. Knowledge graph enhanced recommender system[J]. In ArXiv 2021.