Cross-domain Recommendation with Behavioral Importance Perception

Author:

Chen Hong1ORCID,Wang Xin1ORCID,Xie Ruobing2ORCID,Zhou Yuwei1ORCID,Zhu Wenwu1ORCID

Affiliation:

1. Department of Computer Science and Technology, Tsinghua University, China

2. WeChat Search Application Department, Tencent, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

ACM

Reference33 articles.

1. Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

2. Hong Chen , Xin Wang , Chaoyu Guan , Yue Liu , and Wenwu Zhu . 2022 . Auxiliary Learning with Joint Task and Data Scheduling. In International Conference on Machine Learning, ICML 2022 , 17-23 July 2022, Baltimore, Maryland, USA(Proceedings of Machine Learning Research, Vol. 162). PMLR, 3634–3647. Hong Chen, Xin Wang, Chaoyu Guan, Yue Liu, and Wenwu Zhu. 2022. Auxiliary Learning with Joint Task and Data Scheduling. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA(Proceedings of Machine Learning Research, Vol. 162). PMLR, 3634–3647.

3. Hong Chen Xin Wang Yue Liu Yuwei Zhou Chaoyu Guan and Wenwu Zhu. 2022. Module-Aware Optimization for Auxiliary Learning. In Advances in Neural Information Processing Systems. Hong Chen Xin Wang Yue Liu Yuwei Zhou Chaoyu Guan and Wenwu Zhu. 2022. Module-Aware Optimization for Auxiliary Learning. In Advances in Neural Information Processing Systems.

4. Justin Domke. 2012. Generic methods for optimization-based modeling. In Artificial Intelligence and Statistics. PMLR 318–326. Justin Domke. 2012. Generic methods for optimization-based modeling. In Artificial Intelligence and Statistics. PMLR 318–326.

5. Chelsea Finn , Pieter Abbeel , and Sergey Levine . 2017 . Model-agnostic meta-learning for fast adaptation of deep networks . In International conference on machine learning. PMLR, 1126–1135 . Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning. PMLR, 1126–1135.

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