Fetching the paper…
Reading the bibliography…
Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system.
The Influence Curve and Its Role in Robust Estimation
Frank R. Hampel. 1974 · 1974
Earlier work this paper cites.
Explaining data-driven document classifications
David Martens and Foster Provost. 2014 · 2014
Earlier work this paper cites.
The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan. 2015 · 2015
Earlier work this paper cites.
“Why should I trust you?” Explaining the predictions of any classifier. In KDD
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Neural Collaborative Filtering. In WWW
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Understanding Black-box Predictions via Influence Functions. In ICML
Pang Wei Koh and Percy Liang. 2017 · 2017
Earlier work this paper cites.
A Unified Approach to Interpreting Model Predictions. In NIPS
Scott M Lundberg and Su-In Lee. 2017 · 2017
Earlier work this paper cites.
Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction. In RecSys
Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017 · 2017
Earlier work this paper cites.
Learning heterogeneous knowledge base embeddings for explainable recommendation
Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018 · 2018
Earlier work this paper cites.
Towards interpretation of recommender systems with sorted explanation paths. In ICDM
Fan Yang, Ninghao Liu, Suhang Wang, and Xia Hu. 2018 · 2018
Earlier work this paper cites.
Transparent, scrutable and explainable user models for personalized recommendation. In SIGIR
Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019 · 2019
Cited alongside, same era.
Personalized Fashion Recommendation with Visual Explanations Based on Multimodal Attention Network: Towards Visually Explainable Recommendation. In SIGIR
Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2019 · 2019
Cited alongside, same era.
Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis. In KDD
Weiyu Cheng, Yanyan Shen, Linpeng Huang, and Yanmin Zhu. 2019 · 2019
Cited alongside, same era.
FAIRY: A Framework for Understanding Relationships between Users’ Actions and their Social Feeds. In WSDM
Azin Ghazimatin, Rishiraj Saha Roy, and Gerhard Weikum. 2019 · 2019
Cited alongside, same era.
Attention is not Explanation. In NAACL
Sarthak Jain and Byron C. Wallace. 2019 · 2019
Cited alongside, same era.
Personalized reason generation for explainable song recommendation
Guoshuai Zhao, Hao Fu, Ruihua Song, Tetsuya Sakai, Zhongxia Chen, Xing Xie, and Xueming Qian. 2019 · 2019
Later among the works it cites.
Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations. In SIGIR
Krisztian Balog and Filip Radlinski. 2020 · 2020
Later among the works it cites.
PRINCE: Provider-Side Interpretability with Counterfactual Explanations in Recommender Systems. In WSDM
Azin Ghazimatin, Oana Balalau, Rishiraj Saha Roy, and Gerhard Weikum. 2020 · 2020
Later among the works it cites.
Gaussian Error Linear Units (GELUs)
Dan Hendrycks and Kevin Gimpel. 2020 · 2020
Later among the works it cites.
Deep Critiquing for VAE-Based Recommender Systems. In SIGIR
Kai Luo, Hojin Yang, Ga Wu, and Scott Sanner. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Attention is not not Explanation. In EMNLP-IJCNLP
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
Cited alongside, same era.
A Context-Aware User-Item Representation Learning for Item Recommendation
Libing Wu, Cong Quan, Chenliang Li, Qian Wang, Bolong Zheng, and Xiangyang Luo. 2019 · 2019
Cited alongside, same era.
Reinforcement knowledge graph reasoning for explainable recommendation. In SIGIR
Yikun Xian, Zuohui Fu, S Muthukrishnan, Gerard De Melo, and Yongfeng Zhang. 2019 · 2019
Cited alongside, same era.
Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. In SIGIR
Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. 2019 · 2019
Cited alongside, same era.
Deep Learning Based Recommender System: A Survey and New Perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019 · 2019
Cited alongside, same era.
Neural attentional rating regression with review-level explanations. In WWW
Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018b
Cited in the paper.
Learning to explain: An information-theoretic perspective on model interpretation. In ICML
Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan. 2018a
Cited in the paper.
Explaining machine learning classifiers through diverse counterfactual explanations. In FACCT
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan. 2020 · 2020
Later among the works it cites.
Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang and Xu Chen. 2020 · 2020
Later among the works it cites.
ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models
Azin Ghazimatin, Soumajit Pramanik, Rishiraj Saha Roy, and Gerhard Weikum. 2021 · 2021
Closest in time.
Algorithmic recourse: From counterfactual explanations to interventions. In FACCT
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera. 2021 · 2021
Closest in time.
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, and Fabrizio Silvestri. 2021 · 2021
Closest in time.