Cross-reconstructed Augmentation for Dual-target Cross-domain Recommendation-Reference-Cited by-同舟云学术

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Cross-reconstructed Augmentation for Dual-target Cross-domain Recommendation

Published:2024-07-10 Issue: Volume:34 Page:2352-2356
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Mao Qingyang¹^ORCID,Liu Qi²^ORCID,Li Zhi³^ORCID,Wu Likang¹^ORCID,Lv Bing⁴^ORCID,Zhang Zheng¹^ORCID

Affiliation:

1. State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China

2. State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China & Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

3. Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

4. College of Management and Economics, Tianjin University, Tianjin, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

China Postdoctoral Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657902

Reference48 articles.

1. Jiangxia Cao Shaoshuai Li Bowen Yu Xiaobo Guo Tingwen Liu and Bin Wang. 2023. Towards Universal Cross-Domain Recommendation. In WSDM. 78--86.

2. DisenCDR

3. User-specific Adaptive Fine-tuning for Cross-domain Recommendations

4. Exploiting data sparsity in secure cross-platform social recommendation;Cui Jinming;NeurIPS,2021

5. Jingtao Ding Guanghui Yu Xiangnan He Yuhan Quan Yong Li Tat-Seng Chua Depeng Jin and Jiajie Yu. 2018. Improving Implicit Recommender Systems with View Data. In IJCAI. 3343--3349.

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