Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation-Reference-Cited by-同舟云学术

Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation

Published:2020-07-25 Issue: Volume: Page:
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Container-title:Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Krishnan Adit¹,Das Mahashweta²,Bendre Mangesh²,Yang Hao²,Sundaram Hari¹

Affiliation:

1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

2. Visa Research, Palo Alto, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3397271.3401078

Reference50 articles.

1. Matrix factorization techniques for context aware recommendation

2. Latent Cross

3. Haw-Shiuan Chang Erik Learned-Miller and Andrew McCallum. 2017. Active bias: Training more accurate neural networks by emphasizing high variance samples. In Advances in Neural Information Processing Systems. 1002--1012. Haw-Shiuan Chang Erik Learned-Miller and Andrew McCallum. 2017. Active bias: Training more accurate neural networks by emphasizing high variance samples. In Advances in Neural Information Processing Systems. 1002--1012.

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