└── README.md


/README.md:
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 1 | # Must-read papers on KG/KG4Rec
 2 | Knowledge-aware recommendation papers.
 3 | 
 4 | ### Survey papers:
 5 | 1. **Personalized Entity Recommendation: A Heterogeneous Information Network Approach.**
 6 | *Xiao Yu, et al.* WSDM, 2014. [paper](http://hanj.cs.illinois.edu/pdf/wsdm14_xyu.pdf)
 7 | 
 8 | 1. **Collaborative Knowledge Base Embedding for Recommender Systems.**
 9 | *Fuzheng Zhang, et al.* KDD, 2016. [paper](https://www.kdd.org/kdd2016/papers/files/adf0066-zhangA.pdf)
10 | 
11 | 1. **RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems.**
12 | *Wang Hongwei, et al.* CIKM, 2018. [paper](https://arxiv.org/pdf/1803.03467.pdf)
13 | 
14 | 1. **DKN: Deep knowledge-aware network for news recommendation.**
15 | *Wang Hongwei, et al.* WWW, 2018. [paper](https://arxiv.org/abs/1801.08284)
16 | 
17 | 1. **Improving sequential recommendation with knowledge-enhanced memory networks.**
18 | *Huang Jin, et al.* SIGIR, 2018. [paper](https://dl.acm.org/citation.cfm?doid=3209978.3210017)
19 | 
20 | 1. **Learning over Knowledge-Base Embeddings for Recommendation.**
21 | *Zhang Yongfeng, et al.* SIGIR, 2018. [paper](https://arxiv.org/abs/1803.06540)
22 | 
23 | 1. **Explainable Reasoning over Knowledge Graphs for Recommendation.**
24 | *Wang Xiang, et al.* AAAI, 2019. [paper](https://arxiv.org/abs/1811.04540)
25 | 
26 | 1. **Explainable Recommendation Through Attentive Multi-View Learning.**
27 | *Gao Jingyue, et al.* AAAI, 2019. [paper](https://www.microsoft.com/en-us/research/uploads/prod/2018/10/exrec-aaai-camera-ready.pdf)
28 | 
29 | 1. **Taxonomy-aware multi-hop reasoning networks for sequential recommendation.**
30 | *Huang Jin, et al.* WSDM, 2019. [paper](https://dl.acm.org/citation.cfm?id=3290972)
31 | 
32 | 1. **Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences.**
33 | *Cao Yixin, et al.* WWW, 2019. [paper](https://www.comp.nus.edu.sg/~xiangnan/papers/www19-KGRec.pdf)
34 | 
35 | 1. **Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation.**
36 | *Wang Hongwei, et al.* WWW, 2019 [paper](https://arxiv.org/abs/1901.08907) [code](https://github.com/hwwang55/MKR)
37 | 
38 | 1. **Adversarial Distillation for Efficient Recommendation with External Knowledge.**
39 | *Chen Xu, et al.* TOIS, 2018 [paper](https://dl.acm.org/citation.cfm?id=3281659)
40 | 
41 | 1. **Knowledge Graph Convolutional Networks for Recommender Systems.**
42 | *Wang Hongwei, et al.* WWWW, 2019 [paper](https://arxiv.org/abs/1904.12575)
43 | 
44 | 1. **Dynamic Explainable Recommendation based on Neural Attentive Models.**
45 | *Chen Xu, et al.* AAAI, 2019 [paper](http://yongfeng.me/attach/dynamic-explainable-recommendation.pdf)
46 | 
47 | 1. **Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization.**
48 | *Wang Hongwei, et al.* KDD, 2019 [paper](https://arxiv.org/abs/1905.04413)
49 | 
50 | 1. **KGAT: Knowledge Graph Attention Network for Recommendation.**
51 | *Wang Xiang, et al.* KDD, 2019 [paper](https://arxiv.org/abs/1905.07854) [code](https://github.com/xiangwang1223/knowledge_graph_attention_network)
52 | 
53 | 1. **Reinforcement Knowledge Graph Reasoning for Explainable Recommendation.**
54 | *Xian Yikun and Fu, Zuohui, et al.* SIGIR, 2019 [paper](https://arxiv.org/pdf/1906.05237.pdf)
55 | 
56 | 1. **Conceptualize and Infer User Needs in E-commerce.**
57 | *Luo Xusheng, et al.* CIKM, 2019 [paper](https://arxiv.org/abs/1910.03295)
58 | 
59 | 1. **Attentive Knowledge Graph Embedding for Personalized Recommendation.**
60 | *Xiao Sha* arXiv, 2019 [paper](https://arxiv.org/abs/1910.08288)
61 | 
62 | 1. **Towards Knowledge-Based Recommender Dialog System.**
63 | *Qibin Chen, Jie Tang, et al.* EMNLP, 2019 [paper](https://arxiv.org/pdf/1908.05391.pdf) [code](https://github.com/THUDM/KBRD) 
64 | 
65 | 1. **KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation.** *Pengfei Wang, Yu Fan, Long Xia, Wayne Xin Zhao, et al.* SIGIR, 2020
66 | 
67 | 1. **ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation** *Yufei Feng, Binbin Hu, Fuyu Lv, et al.* SIGIR, 2020
68 | 
69 | ### Tutorials:
70 | 1. **Challenges and Innovations in Building a Product Knowledge Graph.** [slides](http://lunadong.com/talks/PG.pdf)
71 | 
72 | 1. **基于知识的推荐与可解释推荐.** [slides](https://www.researchgate.net/profile/Yongfeng_Zhang/publication/310575130_Explainable_Recommendation_-_Theory_and_Applications/links/59dc32fe0f7e9b1460fc37c7/Explainable-Recommendation-Theory-and-Applications.pdf)
73 | 
74 | ### Open Sources:
75 | 1. [**KGEebedding**](https://github.com/thunlp/OpenKE)
76 | 
77 | ### Datasets:
78 | 1. [**KB4REC**](https://github.com/RUCDM/KB4Rec)
79 | 
80 | ### Personal sites:
81 | 1. [Hongwei Wang](https://hwwang55.github.io/)
82 | 
83 | 1. [Xing Xie](https://www.microsoft.com/en-us/research/people/xingx/)
84 | 
85 | 1. [Yongfeng Zhang](http://yongfeng.me/)
86 | 
87 | 1. [Xiangnan He](https://www.comp.nus.edu.sg/~xiangnan/)
88 | 
89 | 1. [Unknown](http://shomy.top/2019/03/19/kg-ns-recsys/)
90 | 


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