AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation-Reference-Cited by-同舟云学术

AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation

Published:2023-10-21 Issue: Volume: Page:
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Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Jiang Juyong¹^ORCID,Zhang Peiyan²^ORCID,Luo Yingtao³^ORCID,Li Chaozhuo⁴^ORCID,Kim Jae Boum²^ORCID,Zhang Kai⁵^ORCID,Wang Senzhang⁶^ORCID,Xie Xing⁴^ORCID,Kim Sunghun¹^ORCID

Affiliation:

1. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

2. The Hong Kong University of Science and Technology, Hong Kong, China

3. Carnegie Mellon University, Pittsburgh, PA, USA

4. Microsoft Research Asia, Beijing, China

5. East China Normal University, Shanghai, China

6. Central South University, Changsha, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3614773

Reference69 articles.

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3. Behavior sequence transformer for e-commerce recommendation in Alibaba

4. Session-Based Recommendation with Local Invariance

5. Sung Min Cho , Eunhyeok Park , and Sungjoo Yoo . 2020 . MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation. In Fourteenth ACM Conference on Recommender Systems. 515--520 . Sung Min Cho, Eunhyeok Park, and Sungjoo Yoo. 2020. MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation. In Fourteenth ACM Conference on Recommender Systems. 515--520.

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