Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-Based Learning
Paper
Chen, Lvying, Zhang, Ji, Zhang, Yuxi, Yu, Sujie and Li, Bohan. 2025. "Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-Based Learning." 25th International Conferenc on Web Information Systems Engineering (WISE 2024). Doha, Qatar 02 - 05 Dec 2024 Singapore. Springer. https://doi.org/10.1007/978-981-96-0570-5_6
Paper/Presentation Title | Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-Based Learning |
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Presentation Type | Paper |
Authors | Chen, Lvying, Zhang, Ji, Zhang, Yuxi, Yu, Sujie and Li, Bohan |
Journal or Proceedings Title | Proceedings of the 25th International Conferenc on Web Information Systems Engineering (WISE 2024) |
Journal Citation | 15438, pp. 75-90 |
Number of Pages | 75-90 |
Year | 2025 |
Publisher | Springer |
Place of Publication | Singapore |
ISSN | 0302-9743 |
1611-3349 | |
ISBN | 9789819605699 |
9789819605705 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-96-0570-5_6 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-96-0570-5_6 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-96-0570-5 |
Conference/Event | 25th International Conferenc on Web Information Systems Engineering (WISE 2024) |
Event Details | 25th International Conferenc on Web Information Systems Engineering (WISE 2024) Delivery In person Event Date 02 to end of 05 Dec 2024 Event Location Doha, Qatar |
Abstract | Cross-domain sequential recommendation (CDSR) aims to predict user-item interactions from historical sequences across domains. Current CDSR approaches mainly focus on leveraging intrinsic connections among items to capture the dependencies across domains for representation learning. However, these approaches still exist major limitations, including: (1) Extensive CDSR methods overlook temporal dynamics, failing to utilize evolving sequential patterns of user-item interactions. (2) Irrelevant features from source domain to target domain lead to negative transfer of user preferences. To overcome these challenges, we propose an innovative Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-based Learning (TP-CDSR). It features a temporal encoding module that captures the evolving sequences of user interactions by considering the temporal effects as kernels. The projection mechanism learns domain-specific matrices to map the user and item representations across domains, which can reduce migration of redundant features. Comprehensive experiments on two real datasets confirm that TP-CDSR achieves superior results compared to various state-of-the-art recommendation algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. |
Keywords | Contrastive Learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | Nanjing University of Aeronautics and Astronautics, China |
University of Southern Queensland | |
Ministry of Education, China | |
Ministry of Industrial and Information Technology, China |
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https://research.usq.edu.au/item/zx20y/cross-domain-sequential-recommendation-with-temporal-encoding-and-projection-based-learning
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