MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection-Reference-Cited by-同舟云学术

MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection

Published:2020-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of The Web Conference 2020
language:
Short-container-title:

Author:

Luo Mi¹,Chen Fei¹,Cheng Pengxiang¹,Dong Zhenhua¹,He Xiuqiang¹,Feng Jiashi²,Li Zhenguo¹

Affiliation:

1. Huawei Noah's Ark Lab

2. National University of Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3366423.3379999

Reference41 articles.

1. Marcin Andrychowicz Misha Denil Sergio Gomez Matthew W Hoffman David Pfau Tom Schaul and Nando de Freitas. 2016. Learning to learn by gradient descent by gradient descent. In NIPS. Marcin Andrychowicz Misha Denil Sergio Gomez Matthew W Hoffman David Pfau Tom Schaul and Nando de Freitas. 2016. Learning to learn by gradient descent by gradient descent. In NIPS.

2. Simple and Scalable Response Prediction for Display Advertising

3. Fei Chen Mi Luo Zhenhua Dong Zhenguo Li and Xiuqiang He. 2018. Federated Meta-Learning with Fast Convergence and Efficient Communication. arXiv preprint arXiv:1802.07876(2018). Fei Chen Mi Luo Zhenhua Dong Zhenguo Li and Xiuqiang He. 2018. Federated Meta-Learning with Fast Convergence and Efficient Communication. arXiv preprint arXiv:1802.07876(2018).

4. Wei-Yu Chen Yen-Cheng Liu Zsolt Kira Yu-Chiang Frank Wang and Jia-Bin Huang. 2019. A closer look at few-shot classification. arXiv preprint arXiv:1904.04232(2019). Wei-Yu Chen Yen-Cheng Liu Zsolt Kira Yu-Chiang Frank Wang and Jia-Bin Huang. 2019. A closer look at few-shot classification. arXiv preprint arXiv:1904.04232(2019).

Cited by 25 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LiMAML: Personalization of Deep Recommender Models via Meta Learning;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. TLSTSRec: Time-aware long short-term attention neural network for sequential recommendation;Intelligent Data Analysis;2024-08-01

3. Modeling User Attention in Music Recommendation;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

4. Diffusion Recommendation with Implicit Sequence Influence;Companion Proceedings of the ACM Web Conference 2024;2024-05-13

5. Federated recommender systems based on deep learning: The experimental comparisons of deep learning algorithms and federated learning aggregation strategies;Expert Systems with Applications;2024-04