Pre-Training with Transferable Attention for Addressing Market Shifts in Cross-Market Sequential Recommendation-Reference-Cited by-同舟云学术

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Pre-Training with Transferable Attention for Addressing Market Shifts in Cross-Market Sequential Recommendation

Published:2024-08-24 Issue: Volume: Page:2970-2979
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Chen¹^ORCID,Fan Ziwei²^ORCID,Yang Liangwei³^ORCID,Yang Mingdai⁴^ORCID,Liu Xiaolong¹^ORCID,Liu Zhiwei³^ORCID,Yu Philip⁵^ORCID

Affiliation:

1. University of Illinois Chicago, Chicago, IL, USA

2. Amazon, Santa Clara, CA, USA

3. Salesforce AI Research, Palo Alto, CA, USA

4. The University of Chicago, Chicago, IL, USA

5. Tsinghua University, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671698

Reference41 articles.

1. Algorithmic Effects on the Diversity of Consumption on Spotify

2. Samarth Bhargav, Mohammad Aliannejadi, and Evangelos Kanoulas. 2023. Market-Aware Models for Efficient Cross-Market Recommendation. In ECIR (1) (Lecture Notes in Computer Science, Vol. 13980). Springer, 134--149.

3. Cross-Market Product Recommendation

4. Jiangxia Cao, Xin Cong, Tingwen Liu, and Bin Wang. [n.,d.]. Item Similarity Mining for Multi-Market Recommendation. In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

5. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1). Association for Computational Linguistics, 4171--4186.

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