Recurrent-optimized user association representation for multi-target cross-domain sequential recommendation.
Saved in:
| Title: | Recurrent-optimized user association representation for multi-target cross-domain sequential recommendation. |
|---|---|
| Authors: | Chen, Nuo1 (AUTHOR) 21nchen@stu.edu.cn, Zheng, Lin1,2 (AUTHOR) lzheng@stu.edu.cn, Chen, Sentao1 (AUTHOR) sentaochen@stu.edu.cn |
| Source: | Knowledge & Information Systems. Jun2025, Vol. 67 Issue 6, p5077-5103. 27p. |
| Subjects: | Sequential learning, Research personnel, Scalability, Containers |
| Abstract: | In recent years, research on multi-target cross-domain recommendation and sequential recommendation has advanced significantly, and their combination has attracted increasing attention from researchers. The challenge of cross-domain recommendation lies in how to balance the relationship between domain-common features and domain-specific features. Meanwhile, the difficulty of sequential recommendation is how to integrate item features with user representations to improve performance, which makes cross-domain sequential recommendation face greater challenges. To address the challenges in these two fields, we enhance the reusability of user representations through extracting two categories of user association representations as feature containers for cross-domain sequential recommendation. Based on this, we propose a recurrent-balance learning approach to optimize and balance the two user representations, achieving both fine-grained user modeling in sequential recommendation and balanced feature learning in cross-domain recommendation. When the number of domains increases, our method has good scalability through fine-tuning. Experimental results demonstrate the advantages of recurrent-balance learning, and our model outperforms current state-of-the-art cross-domain recommendation methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | In recent years, research on multi-target cross-domain recommendation and sequential recommendation has advanced significantly, and their combination has attracted increasing attention from researchers. The challenge of cross-domain recommendation lies in how to balance the relationship between domain-common features and domain-specific features. Meanwhile, the difficulty of sequential recommendation is how to integrate item features with user representations to improve performance, which makes cross-domain sequential recommendation face greater challenges. To address the challenges in these two fields, we enhance the reusability of user representations through extracting two categories of user association representations as feature containers for cross-domain sequential recommendation. Based on this, we propose a recurrent-balance learning approach to optimize and balance the two user representations, achieving both fine-grained user modeling in sequential recommendation and balanced feature learning in cross-domain recommendation. When the number of domains increases, our method has good scalability through fine-tuning. Experimental results demonstrate the advantages of recurrent-balance learning, and our model outperforms current state-of-the-art cross-domain recommendation methods. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 02191377 |
| DOI: | 10.1007/s10115-025-02371-z |
