Towards Universal Cross-Domain Recommendation-Reference-Cited by-同舟云学术

Towards Universal Cross-Domain Recommendation

Published:2023-02-27 Issue: Volume: Page:
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Container-title:Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
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Author:

Cao Jiangxia¹^ORCID,Li Shaoshuai²^ORCID,Yu Bowen³^ORCID,Guo Xiaobo²^ORCID,Liu Tingwen¹^ORCID,Wang Bin⁴^ORCID

Affiliation:

1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

2. MYbank, Ant Group, Beijing, China

3. DAMO Academy, Alibaba Group, Beijing, China

4. Xiaomi AI Lab, Xiaomi Inc., NEED, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539597.3570366

Reference51 articles.

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3. Hamed Bonab , Mohammad Aliannejadi , Ali Vardasbi , Evangelos Kanoulas , and James Allan . 2021 . Cross-Market Product Recommendation. In ACM International Conference on Information and Knowledge Management (CIKM). Hamed Bonab, Mohammad Aliannejadi, Ali Vardasbi, Evangelos Kanoulas, and James Allan. 2021. Cross-Market Product Recommendation. In ACM International Conference on Information and Knowledge Management (CIKM).

4. Jiangxia Cao , Xin Cong , Tingwen Liu , and Bin Wang . 2022 a. Item Similarity Mining for Multi-Market Recommendation. In ACM International Conference on Research on Development in Information Retrieval (SIGIR). Jiangxia Cao, Xin Cong, Tingwen Liu, and Bin Wang. 2022a. Item Similarity Mining for Multi-Market Recommendation. In ACM International Conference on Research on Development in Information Retrieval (SIGIR).

5. DisenCDR

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