Relation-aware Meta-learning for E-commerce Market Segment Demand Prediction with Limited Records-Reference-Cited by-同舟云学术

Relation-aware Meta-learning for E-commerce Market Segment Demand Prediction with Limited Records

Published:2021-03-08 Issue: Volume: Page:
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Container-title:Proceedings of the 14th ACM International Conference on Web Search and Data Mining
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Author:

Shi Jiatu¹,Yao Huaxiu²,Wu Xian³,Li Tong¹,Lin Zedong¹,Wang Tengfei¹,Zhao Binqiang¹

Affiliation:

1. Alibaba Group, Hangzhou, China

2. Pennsylvania State University, Palo Alto, PA, USA

3. University of Notre Dame, South Bend, IN, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3437963.3441750

Reference37 articles.

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2. XGBoost

3. Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In ICML . 1126--1135. Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In ICML . 1126--1135.

4. Chelsea Finn and Sergey Levine. 2018. Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm. In ICLR . Chelsea Finn and Sergey Levine. 2018. Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm. In ICLR .

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