Mixed Attention Network for Cross-domain Sequential Recommendation-Reference-Cited by-同舟云学术

Mixed Attention Network for Cross-domain Sequential Recommendation

Published:2024-03-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Lin Guanyu¹^ORCID,Gao Chen²^ORCID,Zheng Yu²^ORCID,Chang Jianxin³^ORCID,Niu Yanan³^ORCID,Song Yang³^ORCID,Gai Kun⁴^ORCID,Li Zhiheng²^ORCID,Jin Depeng²^ORCID,Li Yong²^ORCID,Wang Meng⁵^ORCID

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA, USA

2. Tsinghua University, Beijing, China

3. Kuaishou Technology, Beijing, China

4. Unaffiliated, Beijing, China

5. Hefei Comprehensive National Science Center, Hefei, China

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635801

Reference48 articles.

1. Iván Cantador, Ignacio Fernández-Tobías, Shlomo Berkovsky, and Paolo Cremonesi. 2015. Cross-domain recommender systems. In Recommender systems handbook. Springer, 919--959.

2. Sequential Recommendation with Graph Neural Networks

3. Dual Attention Transfer in Session-based Recommendation with Multi-dimensional Integration

4. Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014.

5. Cross-domain Recommendation Without Sharing User-relevant Data

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