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Highly skewed long-tail item distribution is very common in recommendation systems.
An analysis for unreplicated fractional factorials
George EP Box and R Daniel Meyer. 1986 · 1986
Earlier work this paper cites.
SMOTE: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. 2002 · 2002
Earlier work this paper cites.
Amazon. com recommendations: Item-to-item collaborative filtering
Greg Linden, Brent Smith, and Jeremy York. 2003 · 2003
Earlier work this paper cites.
Adaptive importance sampling to accelerate training of a neural probabilistic language model
Yoshua Bengio and Jean-Sébastien Senécal. 2008 · 2008
Earlier work this paper cites.
ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) . IEEE
Haibo He, Yang Bai, Edwardo A Garcia, and Shutao Li. 2008 · 2008
Earlier work this paper cites.
The long tail of recommender systems and how to leverage it. In Proceedings of the 2008 ACM conference on Recommender systems
Yoon-Joo Park and Alexander Tuzhilin. 2008 · 2008
Earlier work this paper cites.
Curriculum learning. In Proceedings of the 26th annual international conference on machine learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
Earlier work this paper cites.
Learning from imbalanced data
Haibo He and Edwardo A Garcia. 2009 · 2009
Earlier work this paper cites.
Analyzing user modeling on twitter for personalized news recommendations. In international conference on user modeling, adaptation, and personalization . Springer
Fabian Abel, Qi Gao, Geert-Jan Houben, and Ke Tao. 2011 · 2011
Earlier work this paper cites.
Sparsity-tolerated algorithm with missing value recovering in user-based collaborative filtering recommendation
Fengjing Yin, Zhenwen Wang, Wentang Tan, and Weidong Xiao. 2012b · 2012
Earlier work this paper cites.
Challenging the long tail recommendation
Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. 2012a · 2012
Earlier work this paper cites.
Combining usage and content in an online recommendation system for music in the long tail
Marcos Aurélio Domingues, Fabien Gouyon, Alípio Mário Jorge, José Paulo Leal, João Vinagre, Luís Lemos, and Mohamed Sordo. 2013 · 2013
Earlier work this paper cites.
Text classification by augmenting bag of words (BOW) representation with co-occurrence feature
K Soumya George and Shibily Joseph. 2014 · 2014
Earlier work this paper cites.
Accelerating t-SNE using tree-based algorithms
Laurens Van Der Maaten. 2014 · 2014
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent. In Advances in neural information processing systems
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas. 2016 · 2016
Earlier work this paper cites.
Recommended for you: The Netflix Prize and the production of algorithmic culture
Blake Hallinan and Ted Striphas. 2016 · 2016
Earlier work this paper cites.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle. 2016 · 2016
Earlier work this paper cites.
Meta-learning with memory-augmented neural networks. In International conference on machine learning
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap. 2016 · 2016
Earlier work this paper cites.
Learning to learn: Model regression networks for easy small sample learning. In European Conference on Computer Vision . Springer
Yu-Xiong Wang and Martial Hebert. 2016 · 2016
Cited alongside, same era.
Controlling popularity bias in learning-to-rank recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017 · 2017
Cited alongside, same era.
Beyond globally optimal: Focused learning for improved recommendations. In Proceedings of the 26th International Conference on World Wide Web
Alex Beutel, Ed H Chi, Zhiyuan Cheng, Hubert Pham, and John Anderson. 2017 · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks. In ICML
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Cited alongside, same era.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Meta-transfer learning for few-shot learning. In Proceedings of the IEEE conference on computer vision and pattern recognition
Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele. 2019 · 2019
Later among the works it cites.
Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems
Ruoxi Wang, Zhe Zhao, Xinyang Yi, Ji Yang, Derek Zhiyuan Cheng, Lichan Hong, Steve Tjoa, Jieqi Kang, Evan Ettinger, and H Chi. 2019b · 2019
Later among the works it cites.
Sampling-bias-corrected neural modeling for large corpus item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems
Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed Chi. 2019 · 2019
Later among the works it cites.
Addressing the item cold-start problem by attribute-driven active learning
Yu Zhu, Jinghao Lin, Shibi He, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2019 · 2019
Later among the works it cites.
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Cited alongside, same era.
Prototypical networks for few-shot learning. In Advances in neural information processing systems
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
Cited alongside, same era.
Dropoutnet: Addressing cold start in recommender systems. In Advances in neural information processing systems
Maksims Volkovs, Guangwei Yu, and Tomi Poutanen. 2017 · 2017
Cited alongside, same era.
Learning to model the tail. In Advances in Neural Information Processing Systems
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert. 2017 · 2017
Cited alongside, same era.
Large scale fine-grained categorization and domain-specific transfer learning. In Proceedings of the IEEE conference on computer vision and pattern recognition
Yin Cui, Yang Song, Chen Sun, Andrew Howard, and Serge Belongie. 2018 · 2018
Cited alongside, same era.
Personal price aware multi-seller recommender system: Evidence from eBay
Asnat Greenstein-Messica and Lior Rokach. 2018 · 2018
Cited alongside, same era.
Few-shot human motion prediction via meta-learning. In Proceedings of the European Conference on Computer Vision (ECCV)
Liang-Yan Gui, Yu-Xiong Wang, Deva Ramanan, and José MF Moura. 2018 · 2018
Cited alongside, same era.
Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
Jiaxi Tang and Ke Wang. 2018 · 2018
Cited alongside, same era.
Jason Brownlee. 2020 · 2020
Closest in time.
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Manqing Dong, Feng Yuan, Lina Yao, Xiwei Xu, and Liming Zhu. 2020 · 2020
Closest in time.
Student-teacher curriculum learning via reinforcement learning: predicting hospital inpatient admission location. In ICML
Rasheed El-Bouri, David Eyre, Peter Watkinson, Tingting Zhu, and David Clifton. 2020 · 2020
Closest in time.
Breaking the curse of space explosion: Towards efficient NAS with curriculum search. In ICML
Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, and Mingkui Tan. 2020 · 2020
Closest in time.
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems. In Companion Proceedings of the Web Conference 2020
Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen, Xinyang Yi, Dong Lin, Lichan Hong, and Ed H Chi. 2020a · 2020
Closest in time.
Joint Training Capsule Network for Cold Start Recommendation. In SIGIR
Tingting Liang, Congying Xia, Yuyu Yin, and Philip S Yu. 2020 · 2020
Closest in time.
Long-tail Session-based Recommendation. In Fourteenth ACM Conference on Recommender Systems
Siyi Liu and Yujia Zheng. 2020 · 2020
Closest in time.
Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Yuanfu Lu, Yuan Fang, and Chuan Shi. 2020 · 2020
Closest in time.
Off-policy Learning in Two-stage Recommender Systems. In Proceedings of The Web Conference 2020
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, and Ed H Chi. 2020 · 2020
Closest in time.
Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. 2020 · 2020
Closest in time.
Handling incomplete heterogeneous data using vaes
Alfredo Nazabal, Pablo M Olmos, Zoubin Ghahramani, and Isabel Valera. 2020 · 2020
Closest in time.
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Aditya Pal, Chantat Eksombatchai, Yitong Zhou, Bo Zhao, Charles Rosenberg, and Jure Leskovec. 2020 · 2020
Closest in time.
Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations. In Companion Proceedings of the Web Conference 2020
Ji Yang, Xinyang Yi, Derek Zhiyuan Cheng, Lichan Hong, Yang Li, Simon Xiaoming Wang, Taibai Xu, and Ed H Chi. 2020 · 2020
Closest in time.