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DSR-CM: decoupled long-term sequential recommendation model leveraging competitive mechanism.

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Title: DSR-CM: decoupled long-term sequential recommendation model leveraging competitive mechanism.
Authors: Cui, Shaoguo1 (AUTHOR) csg@cqnu.edu.cn, Li, Xingyu1 (AUTHOR) 2022110516030@stu.cqnu.edu.cn
Source: Neural Computing & Applications. May2025, Vol. 37 Issue 15, p8987-9009. 23p.
Subjects: Recommender systems, Learning ability, Computational complexity, Information storage & retrieval systems, Problem solving
Abstract: Existing long-term sequential recommendations ignore the capture of real-time user preferences, which leads to poor recommendation accuracy. To solve this problem, this work proposes a decoupled long-term sequential recommendation model leveraging competitive mechanism (DSR-CM). By introducing a multi-head flow-attention mechanism, the user preference information flow competes spontaneously under the constraints of the competition mechanism, which preserves the learning ability of traditional dot-product attention at the level of linear computational complexity, and captures the dynamic preference relations in the sequence of user behaviours more efficiently. Meanwhile, using decoupled computational position encoding more accurately captures the sequential relationship between user behaviours and models the user preference trends. Extensive experimental studies are carried out on three real-world datasets, where DSR-CM outperforms the existing state-of-the-art methods in terms of both effectiveness and efficiency, it accurately captures user's dynamically changing preferences and latest trends, and explores new ways to enhance the adaptability and accuracy of the recommender system. The implementation code is available online at https://github.com/cyxg7/DSR-CM. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications 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.)
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. May2025, Vol. 37 Issue 15, p8987-9009. 23p.
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  Data: Existing long-term sequential recommendations ignore the capture of real-time user preferences, which leads to poor recommendation accuracy. To solve this problem, this work proposes a decoupled long-term sequential recommendation model leveraging competitive mechanism (DSR-CM). By introducing a multi-head flow-attention mechanism, the user preference information flow competes spontaneously under the constraints of the competition mechanism, which preserves the learning ability of traditional dot-product attention at the level of linear computational complexity, and captures the dynamic preference relations in the sequence of user behaviours more efficiently. Meanwhile, using decoupled computational position encoding more accurately captures the sequential relationship between user behaviours and models the user preference trends. Extensive experimental studies are carried out on three real-world datasets, where DSR-CM outperforms the existing state-of-the-art methods in terms of both effectiveness and efficiency, it accurately captures user's dynamically changing preferences and latest trends, and explores new ways to enhance the adaptability and accuracy of the recommender system. The implementation code is available online at https://github.com/cyxg7/DSR-CM. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computing & Applications 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.</i> (Copyright applies to all Abstracts.)
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