CAFE

[1]  Jing Ma,et al.  Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems , 2017, Knowl. Based Syst..

[2]  Hongwei Wang,et al.  RippleNet , 2018, Proceedings of the 27th ACM International Conference on Information and Knowledge Management.

[3]  Yongfeng Zhang,et al.  Reinforcement Knowledge Graph Reasoning for Explainable Recommendation , 2019, SIGIR.

[4]  Jure Leskovec,et al.  Understanding Behaviors that Lead to Purchasing: A Case Study of Pinterest , 2016, KDD.

[5]  Lei Zheng,et al.  Joint Deep Modeling of Users and Items Using Reviews for Recommendation , 2017, WSDM.

[6]  Yixin Cao,et al.  Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences , 2019, WWW.

[7]  Yongfeng Zhang,et al.  Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation , 2019, SIGIR.

[8]  Lars Schmidt-Thieme,et al.  Multi-relational matrix factorization using bayesian personalized ranking for social network data , 2012, WSDM '12.

[9]  Ji-Rong Wen,et al.  KB4Rec: A Data Set for Linking Knowledge Bases with Recommender Systems , 2018, Data Intelligence.

[10]  William W. Cohen,et al.  Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach , 2016, RecSys.

[11]  Jitao Sang,et al.  Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation , 2019, ACM Multimedia.

[12]  Zhe Zhao,et al.  Improving User Topic Interest Profiles by Behavior Factorization , 2015, WWW.

[13]  Yongfeng Zhang,et al.  Dynamic Explainable Recommendation Based on Neural Attentive Models , 2019, AAAI.

[14]  Xing Xie,et al.  Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs , 2020, SIGIR.

[15]  Chenxi Zhang,et al.  TSCSet: A Crowdsourced Time-Sync Comment Dataset for Exploration of User Experience Improvement , 2018, IUI.

[16]  Yongfeng Zhang,et al.  Explainable Recommendation: A Survey and New Perspectives , 2020 .

[17]  Nicholas Jing Yuan,et al.  Collaborative Knowledge Base Embedding for Recommender Systems , 2016, KDD.

[18]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[19]  Bo Zong,et al.  Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation , 2019, AAAI.

[20]  Jure Leskovec,et al.  Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems , 2019, KDD.

[21]  Edward Y. Chang,et al.  Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks , 2018, SIGIR.

[22]  Gerard de Melo,et al.  ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs , 2020, AAAI.

[23]  Minyi Guo,et al.  DKN: Deep Knowledge-Aware Network for News Recommendation , 2018, WWW.

[24]  Guokun Lai,et al.  Explicit factor models for explainable recommendation based on phrase-level sentiment analysis , 2014, SIGIR.

[25]  Li Chen,et al.  Generate Neural Template Explanations for Recommendation , 2020, CIKM.

[26]  Max Welling,et al.  Modeling Relational Data with Graph Convolutional Networks , 2017, ESWC.