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In an era of information explosion, recommendation systems play an important role in people's daily life by facilitating content exploration.
Causal diagrams for empirical research
Judea Pearl. 1995 · 1995
Earlier work this paper cites.
Multitask Learning
R. Caruana. 1997 · 1997
Earlier work this paper cites.
Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000
Leland Gerson Neuberg. 2003 · 2003
Earlier work this paper cites.
A survey on transfer learning
Sinno Jialin Pan and Qiang Yang. 2009 · 2009
Earlier work this paper cites.
Causality
Judea Pearl. 2009 · 2009
Earlier work this paper cites.
Factorization machines. In 2010 IEEE International conference on data mining . IEEE, 995–1000
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Challenging the Long Tail Recommendation
Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. 2012 · 2012
Earlier work this paper cites.
Image-Based Recommendations on Styles and Substitutes.. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, August 9-13, 2015
Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015 · 2015
Earlier work this paper cites.
Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems . 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Earlier work this paper cites.
Causal inference in statistics: A primer
Madelyn Glymour, Judea Pearl, and Nicholas P Jewell. 2016 · 2016
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
Learning deep representation for imbalanced classification. In Proceedings of the IEEE conference on computer vision and pattern recognition . 5375–5384
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang. 2016 · 2016
Earlier work this paper cites.
Factors in finetuning deep model for object detection with long-tail distribution. In Proceedings of the IEEE conference on computer vision and pattern recognition . 864–873
Wanli Ouyang, Xiaogang Wang, Cong Zhang, and Xiaokang Yang. 2016 · 2016
Earlier work this paper cites.
Multi-objective optimization for long tail recommendation
Shanfeng Wang, Maoguo Gong, Haoliang Li, and Junwei Yang. 2016 · 2016
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Earlier work this paper cites.
Improving the quality of recommendations for users and items in the tail of distribution
Liang Hu, Longbing Cao, Jian Cao, Zhiping Gu, Guandong Xu, and Jie Wang. 2017 · 2017
Cited alongside, same era.
Enhancing long tail item recommendations using tripartite graphs and Markov process. In Proceedings of the International Conference on Web Intelligence . 761–768
Joseph Johnson and Yiu-Kai Ng. 2017 · 2017
Cited alongside, same era.
Learning to model the tail. In Proceedings of the 31st International Conference on Neural Information Processing Systems . 7032–7042
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert. 2017 · 2017
Cited alongside, same era.
Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International Conference on Machine Learning . PMLR, 794–803
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. 2018 · 2018
Cited alongside, same era.
Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM) . IEEE, 197–206
On sampled metrics for item recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 1748–1757
Walid Krichene and Steffen Rendle. 2020 · 2020
Later among the works it cites.
Meta-learning on heterogeneous information networks for cold-start recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1563–1573
Yuanfu Lu, Yuan Fang, and Chuan Shi. 2020 · 2020
Later among the works it cites.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang. 2020a · 2020
Later among the works it cites.
Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang. 2020b · 2020
Later among the works it cites.
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Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Cited alongside, same era.
Recommending long-tail items using extended tripartite graphs. In 2018 IEEE International Conference on Big Knowledge (ICBK) . IEEE, 123–130
Andrew Luke, Joseph Johnson, and Yiu-Kai Ng. 2018 · 2018
Cited alongside, same era.
Online adaptive asymmetric active learning for budgeted imbalanced data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2768–2777
Yifan Zhang, Peilin Zhao, Jiezhang Cao, Wenye Ma, Junzhou Huang, Qingyao Wu, and Mingkui Tan. 2018 · 2018
Cited alongside, same era.
Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1059–1068
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
Cited alongside, same era.
Recommending the long tail items through personalized diversification
Elaheh Malekzadeh Hamedani and Marjan Kaedi. 2019 · 2019
Cited alongside, same era.
Melu: Meta-learned user preference estimator for cold-start recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1073–1082
Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019 · 2019
Cited alongside, same era.
Warm up cold-start advertisements: Improving ctr predictions via learning to learn id embeddings. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 695–704
Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, and Qing He. 2019 · 2019
Cited alongside, same era.
Using multi-objective optimization to solve the long tail problem in recommender system. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 302–313
Jiaona Pang, Jun Guo, and Wei Zhang. 2019 · 2019
Cited alongside, same era.
Junjiao Tian, Yen-Cheng Liu, Nathan Glaser, Yen-Chang Hsu, and Zsolt Kira. 2020 · 2020
Later among the works it cites.
Zirui Wang, Yulia Tsvetkov, Orhan Firat, and Yuan Cao. 2020 · 2020
Later among the works it cites.
Learning transferrable parameters for long-tailed sequential user behavior modeling. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 359–367
Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun, and Steven CH Hoi. 2020 · 2020
Later among the works it cites.
Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. 2020 · 2020
Later among the works it cites.
Domain generalization via gradient surgery. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 6630–6638
Lucas Mansilla, Rodrigo Echeveste, Diego H Milone, and Enzo Ferrante. 2021 · 2021
Later among the works it cites.
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Li Li, Kun Zhang, Jinmei Luo, Zhaojie Liu, and Yanlong Du. 2021 · 2021
Later among the works it cites.
Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 1791–1800
Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021 · 2021
Later among the works it cites.
Deep long-tailed learning: A survey
Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan, and Jiashi Feng. 2021 · 2021
Later among the works it cites.
Learning fast sample re-weighting without reward data. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 725–734
Zizhao Zhang and Tomas Pfister. 2021 · 2021
Later among the works it cites.
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang, Kaikai Ge, Ying Sun, Xu Zhang, Leyu Lin, and Juan Cao. 2021 · 2021
Later among the works it cites.