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Adaptive disentangled learning recommendation via similarity popularity.

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Title: Adaptive disentangled learning recommendation via similarity popularity.
Authors: Ye, Jianmei1,2,3 (AUTHOR) yejm@cqupt.edu.cn, Wang, Heming2 (AUTHOR) s220701027@stu.cqupt.edu.cn, Deng, Jiangzhou1,2,4 (AUTHOR) dengjz@cqupt.edu.cn, Zhang, Zhiqiang2 (AUTHOR) zhangzq@cqupt.edu.cn, Wang, Yong1,2 (AUTHOR) wangyong1@cqupt.edu.cn, Xu, Zeshui3 (AUTHOR) xuzeshui@263.net, Khushvakhtzoda, Kobiljon Kh.5 (AUTHOR) kobil.barfiev@gmail.com
Source: Applied Intelligence. Jan2026, Vol. 56 Issue 1, p1-15. 15p.
Abstract: Learning representations of users and items are importance of predicting user preferences for accurate recommendation. However, confusion features of users genuine interests and conformity behaviors often hamper the accurate representation of user preferences and lead to inappropriate recommendation. The existing methods overlook the fine-grained attributes of interest and conformity, hiding the actual preferences of users. In this paper, we propose a similarity popularity-based adaptive disentangled learning recommendation model with individual preference to learn representations from the observed data with the confusion features of interest and conformity. Firstly, a novel popularity calculation method based on item similarity is presented, which better captures fine-grained attributes and distinguishes user interest and conformity to generate more accurate sampling signals. Subsequently, we design a weight attention score mechanism that dynamically adjusts the weights of interest and conformity, accurately aligning with the individualized preferences of users. Finally, we develop a comprehensive loss function to calibrate the importance of disentangled representation learning. Experiments on public datasets demonstrate that the proposed model outperforms the existing debiasing methods, mitigating the popularity bias of recommendations from fine-grained attributes perspective. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Learning representations of users and items are importance of predicting user preferences for accurate recommendation. However, confusion features of users genuine interests and conformity behaviors often hamper the accurate representation of user preferences and lead to inappropriate recommendation. The existing methods overlook the fine-grained attributes of interest and conformity, hiding the actual preferences of users. In this paper, we propose a similarity popularity-based adaptive disentangled learning recommendation model with individual preference to learn representations from the observed data with the confusion features of interest and conformity. Firstly, a novel popularity calculation method based on item similarity is presented, which better captures fine-grained attributes and distinguishes user interest and conformity to generate more accurate sampling signals. Subsequently, we design a weight attention score mechanism that dynamically adjusts the weights of interest and conformity, accurately aligning with the individualized preferences of users. Finally, we develop a comprehensive loss function to calibrate the importance of disentangled representation learning. Experiments on public datasets demonstrate that the proposed model outperforms the existing debiasing methods, mitigating the popularity bias of recommendations from fine-grained attributes perspective. [ABSTRACT FROM AUTHOR]
ISSN:0924669X
DOI:10.1007/s10489-025-07050-8