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Deep learning-based recommender systems have become an integral part of several online platforms.
Group preference aggregation: A nash equilibrium approach. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE, 679–688
Hongke Zhao, Qi Liu, Yong Ge, Ruoyan Kong, and Enhong Chen. 2016 · 2016
Earlier work this paper cites.
Neural Collaborative Filtering. In Proc. of WWW’17 . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking. In Proc. of KDD’17 . ACM, 465–474
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017 · 2017
Earlier work this paper cites.
Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. In Proc. of SIGIR’19 . ACM, 125–134
Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. 2019 · 2019
Earlier work this paper cites.
Model Extraction from Counterfactual Explanations
Ulrich Aïvodji, Alexandre Bolot, and Sébastien Gambs. 2020 · 2020
Earlier work this paper cites.
Model-Agnostic Counterfactual Explanations for Consequential Decisions. In Proc. of AISTATS’20 , Vol. 108. PMLR, 895–905
Amir-Hossein Karimi, Gilles Barthe, Borja Balle, and Isabel Valera. 2020 · 2020
Earlier work this paper cites.
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. In Proc. of KDD’20 . ACM, 238–248
Thai Le, Suhang Wang, and Dongwon Lee. 2020 · 2020
Earlier work this paper cites.
Dynamic graph collaborative filtering. In 2020 IEEE International Conference on Data Mining (ICDM) . IEEE, 322–331
Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, and S Yu Philip. 2020b · 2020
Earlier work this paper cites.
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations. In Proc. of FAT*’20 . ACM, 607–617
Ramaravind Kommiya Mothilal, Amit Sharma, and Chenhao Tan. 2020 · 2020
Earlier work this paper cites.
Revisiting Adversarially Learned Injection Attacks against Recommender Systems. In Proc. of RecSys’20 . ACM, 318–327
Jiaxi Tang, Hongyi Wen, and Ke Wang. 2020 · 2020
Earlier work this paper cites.
CDLFM: cross-domain recommendation for cold-start users via latent feature mapping
Xinghua Wang, Zhaohui Peng, Senzhang Wang, Philip S Yu, Wenjing Fu, Xiaokang Xu, and Xiaoguang Hong. 2020 · 2020
Cited alongside, same era.
Neural Collaborative Reasoning. In Proc. of TheWebConf’21 . 1516–1527
Hanxiong Chen, Shaoyun Shi, Yunqi Li, and Yongfeng Zhang. 2021 · 2021
Cited alongside, same era.
Data Poisoning Attacks to Deep Learning Based Recommender Systems
Hai Huang, Jiaming Mu, Neil Zhenqiang Gong, Qi Li, Bin Liu, and Mingwei Xu. 2021 · 2021
Cited alongside, same era.
Towards a better understanding of linear models for recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 776–785
Ruoming Jin, Dong Li, Jing Gao, Zhi Liu, Li Chen, and Yang Zhou. 2021 · 2021
Cited alongside, same era.
Learning to ignore: A case study of organization-wide bulk email effectiveness
Ruoyan Kong, Haiyi Zhu, and Joseph A Konstan. 2021 · 2021
Exploiting Explanations for Model Inversion Attacks. In Proc. of ICCV’21 . IEEE, 682–692
Xuejun Zhao, Wencan Zhang, Xiaokui Xiao, and Brian Lim. 2021 · 2021
Later among the works it cites.
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Ziheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, Zhenhua Huang, Hongshik Ahn, and Gabriele Tolomei. 2022a · 2022
Later among the works it cites.
Inferring Sensitive Attributes from Model Explanations. In Proc. of CIKM’22 . ACM, 416–425
Vasisht Duddu and Antoine Boutet. 2022 · 2022
Later among the works it cites.
What is the Solution for State Adversarial Multi-Agent Reinforcement Learning?
Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, and Fei Miao. 2022 · 2022
Later among the works it cites.
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders. In 2022 IEEE International Conference on Big Data (Big Data) . IEEE, 616–625
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Cited alongside, same era.
Tackling Mode Collapse in Multi-Generator GANs with Orthogonal Vectors
Wei Li, Li Fan, Zhenyu Wang, Chao Ma, and Xiaohui Cui. 2021a · 2021
Cited alongside, same era.
Hausdorff GAN: Improving GAN Generation Quality With Hausdorff Metric
Wei Li, Zhixuan Liang, Ping Ma, Ruobei Wang, Xiaohui Cui, and Ping Chen. 2021b · 2021
Cited alongside, same era.
Generating Actionable Interpretations from Ensembles of Decision Trees
Gabriele Tolomei and Fabrizio Silvestri. 2021 · 2021
Cited alongside, same era.
Counterfactual Explanations for Neural Recommenders. In Proc. of SIGIR’21 . ACM, 1627–1631
Khanh Hiep Tran, Azin Ghazimatin, and Rishiraj Saha Roy. 2021 · 2021
Cited alongside, same era.
Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data. In Proc. of KDD’21 . ACM, 2154–2164
Hengtong Zhang, Changxin Tian, Yaliang Li, Lu Su, Nan Yang, Wayne Xin Zhao, and Jing Gao. 2021 · 2021
Cited alongside, same era.
ReLAX: Reinforcement Learning Agent Explainer for Arbitrary Predictive Models. In Proc. of CIKM’22 . ACM, 252–261
Ziheng Chen, Fabrizio Silvestri, Jia Wang, He Zhu, Hongshik Ahn, and Gabriele Tolomei. 2022b
Cited in the paper.
On sampling top-k recommendation evaluation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2114–2124
Dong Li, Ruoming Jin, Jing Gao, and Zhi Liu. 2020a
Cited in the paper.
Xiaohan Li, Zheng Liu, Luyi Ma, Kaushiki Nag, Stephen Guo, S Yu Philip, and Kannan Achan. 2022 · 2022
Later among the works it cites.
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles. In Proc. of AAAI’22 . AAAI Press, 5313–5322
Ana Lucic, Harrie Oosterhuis, Hinda Haned, and Maarten de Rijke. 2022 · 2022
Later among the works it cites.
Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1401–1411
Shanlei Mu, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding, and Ji-Rong Wen. 2022 · 2022
Later among the works it cites.
On the Privacy Risks of Algorithmic Recourse
Martin Pawelczyk, Himabindu Lakkaraju, and Seth Neel. 2022 · 2022
Later among the works it cites.
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks. In Proc. of IJCNN’22 . IEEE, 1–17
Federico Siciliano, Maria Sofia Bucarelli, Gabriele Tolomei, and Fabrizio Silvestri. 2022 · 2022
Later among the works it cites.
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations. In Proc. of FAccT’22 . ACM, 1318–1329
Yongjie Wang, Hangwei Qian, and Chunyan Miao. 2022 · 2022
Later among the works it cites.