Multi-Domain Sequential Recommendation via Domain Space Learning
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- Multi-Domain Sequential Recommendation via Domain Space Learning
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A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSequential recommendation methods play an irreplaceable role in recommender systems which can capture the users’ dynamic preferences from the behavior sequences. Despite their success, these works usually suffer from the sparsity problem commonly ...
A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation
Cross-domain sequential recommendation aims to alleviate the sparsity problem while capturing users’ sequential preferences. However, most existing methods learn the user preferences in each domain separately, and then perform knowledge transfer between ...
Contrastive Multi-view Interest Learning for Cross-domain Sequential Recommendation
Cross-domain recommendation (CDR), which leverages information collected from other domains, has been empirically demonstrated to effectively alleviate data sparsity and cold-start problems encountered in traditional recommendation systems. However, ...
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- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
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Association for Computing Machinery
New York, NY, United States
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- Research-article
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- National Research Foundation of Korea
- Institute for Information and communications Technology Promotion
- Daegu Digital Innovation Promotion Agency
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