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State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models.
Causality: models, reasoning and inference . Vol. 29
Judea Pearl. 2000 · 2000
Earlier work this paper cites.
Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web . ACM, 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling. 2014 · 2014
Earlier work this paper cites.
Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014 · 2014
Earlier work this paper cites.
Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin. 2015 · 2015
Earlier work this paper cites.
Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR conference on Research and Development in Information Retrieval . ACM, 403–412
Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015 · 2015
Earlier work this paper cites.
Fusing similarity models with markov chains for sparse sequential recommendation. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE, 191–200
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks. In International Conference on Learning Representations
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
A dynamic recurrent model for next basket recommendation. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . ACM, 729–732
Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016 · 2016
Earlier work this paper cites.
A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi S Jaakkola. 2017 · 2017
Earlier work this paper cites.
Unbiased learning-to-rank with biased feedback. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining . ACM, 781–789
Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017 · 2017
Cited alongside, same era.
Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM, 1419–1428
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
Cited alongside, same era.
Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In Proceedings of the Eleventh ACM Conference on Recommender Systems . 297–305
Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017 · 2017
Cited alongside, same era.
Designing and implementing transparency for real time inspection of autonomous robots
Andreas Theodorou, Robert H Wortham, and Joanna J Bryson. 2017 · 2017
Cited alongside, same era.
Learning heterogeneous knowledge base embeddings for explainable recommendation
Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . ACM, 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Later among the works it cites.
A Reinforcement Learning Framework for Explainable Recommendation. In 2018 IEEE International Conference on Data Mining (ICDM) . IEEE, 587–596
Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, and Xing Xie. 2018a · 2018
Later among the works it cites.
Challenges of Using Text Classifiers for Causal Inference. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 4586–4598
Zach Wood-Doughty, Ilya Shpitser, and Mark Dredze. 2018 · 2018
Later among the works it cites.
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. In International Conference on Learning Representations
Lars Buesing, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, and Nicolas Heess. 2019 · 2019
Later among the works it cites.
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Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018 · 2018
Cited alongside, same era.
Causal embeddings for recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems . ACM
Stephen Bonner and Flavian Vasile. 2018 · 2018
Cited alongside, same era.
Automatic generation of natural language explanations. In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion . ACM, 57
Felipe Costa, Sixun Ouyang, Peter Dolog, and Aonghus Lawlor. 2018 · 2018
Cited alongside, same era.
Improving sequential recommendation with knowledge-enhanced memory networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . ACM, 505–514
Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y Chang. 2018 · 2018
Cited alongside, same era.
Explore, exploit, and explain: personalizing explainable recommendations with bandits. In Proceedings of the 12th ACM Conference on Recommender Systems . ACM, 31–39
James McInerney, Benjamin Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, and Rishabh Mehrotra. 2018 · 2018
Cited alongside, same era.
Explanation mining: Post hoc interpretability of latent factor models for recommendation systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Georgina Peake and Jun Wang. 2018 · 2018
Cited alongside, same era.
Attention-driven factor model for explainable personalized recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . ACM, 909–912
Jingwu Chen, Fuzhen Zhuang, Xin Hong, Xiang Ao, Xing Xie, and Qing He. 2018b
Cited in the paper.
Sequential recommendation with user memory networks. In Proceedings of the eleventh ACM international conference on web search and data mining . 108–116
Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018a
Cited in the paper.
A Capsule Network for Recommendation and Explaining What You Like and Dislike. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . ACM, 275–284
Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, and Libing Wu. 2019 · 2019
Later among the works it cites.
Jointly learning explainable rules for recommendation with knowledge graph. In The World Wide Web Conference . 1210–1221
Weizhi Ma, Min Zhang, Yue Cao, Woojeong Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, and Xiang Ren. 2019 · 2019
Later among the works it cites.
Explainable reasoning over knowledge graphs for recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 5329–5336
Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. 2019 · 2019
Later among the works it cites.
Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 285–294
Yikun Xian, Zuohui Fu, S Muthukrishnan, Gerard De Melo, and Yongfeng Zhang. 2019 · 2019
Later among the works it cites.
Fairness-Aware Explainable Recommendation over Knowledge Graphs
Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, et al · 2020
Closest in time.
Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang and Xu Chen. 2020 · 2020
Closest in time.