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The ubiquity of implicit feedback makes them the default choice to build online recommender systems.
Identifying mislabeled training data
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Collaborative Filtering for Implicit Feedback Datasets. In
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Deep Learning
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In
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Collaborative denoising auto-encoders for top-n Recommender Systems. In
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Gaze Prediction for Recommender Systems. In
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A closer look at memorization in deep networks. In
Chat more: Deepening and widening the chatting topic via a deep model. In
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Robust Asymmetric Recommendation via Min-Max Optimization. In
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lambdaOpt: Learn to Regularize Recommender Models in Finer Levels. In
Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, and Yue Wang. 2019 · 2019
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MMALFM: Explainable Recommendation by Leveraging Reviews and Images
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Graph adversarial training: Dynamically regularizing based on graph structure
Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua. 2019 · 2019
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Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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The Adressa Dataset for News Recommendation. In
Jon Atle Gulla, Lemei Zhang, Peng Liu, Özlem Özgöbek, and Xiaomeng Su. 2017 · 2017
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Neural Collaborative Filtering. In
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
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Focal loss for dense object detection. In
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017 · 2017
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Social collaborative viewpoint regression with explainable recommendations. In
Zhaochun Ren, Shangsong Liang, Piji Li, Shuaiqiang Wang, and Maarten de Rijke. 2017 · 2017
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Learning on partial-order hypergraphs. In
Fuli Feng, Xiangnan He, Yiqun Liu, Liqiang Nie, and Tat-Seng Chua. 2018 · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels. In
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018 · 2018
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Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yueming Li, Tat-Seng Chua, Lina Yao, Yang Song, and Depeng Jin. 2019 · 2019
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To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. In
Rolf Jagerman, Harrie Oosterhuis, and Maarten de Rijke. 2019 · 2019
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Multimodal dialog system: Generating responses via adaptive decoders. In
Liqiang Nie, Wenjie Wang, Richang Hong, Meng Wang, and Qi Tian. 2019 · 2019
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Evaluating Recommender System Stability with Influence-Guided Fuzzing. In
David Shriver, Sebastian G. Elbaum, Matthew B. Dwyer, and David S. Rosenblum. 2019 · 2019
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Neural Graph Collaborative Filtering. In
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
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Leveraging Post-click Feedback for Content Recommendations. In
Hongyi Wen, Longqi Yang, and Deborah Estrin. 2019 · 2019
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Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020 · 2020
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LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020 · 2020
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What Aspect Do You Like: Multi-scale Time-aware User Interest Modeling for Micro-video Recommendation. In
Hao Jiang, Wenjie Wang, Yinwei Wei, Zan Gao, Yinglong Wang, and Liqiang Nie. 2020 · 2020
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"Click" Is Not Equal to "Like": Counterfactual Recommendation for Mitigating Clickbait Issue
Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2020 · 2020
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