IDBR: Interaction-Aware Dual-Granularity Learning for Bundle Recommendation
In the recommendation system, bundle recommendation is a prevalent sales strategy in which a combination of diverse, related, or complementary products is suggested to consumers. Recent methodologies frequently utilize graph neural networks to capture information from user-bundle, user-item, and bun...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-05-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2025.9020016 |
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| _version_ | 1849714819268083712 |
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| author | Jinqing Wang Yuan Cao Fan Zhang Feifei Kou Kaimin Wei Jinghui Zhang Jinpeng Chen |
| author_facet | Jinqing Wang Yuan Cao Fan Zhang Feifei Kou Kaimin Wei Jinghui Zhang Jinpeng Chen |
| author_sort | Jinqing Wang |
| collection | DOAJ |
| description | In the recommendation system, bundle recommendation is a prevalent sales strategy in which a combination of diverse, related, or complementary products is suggested to consumers. Recent methodologies frequently utilize graph neural networks to capture information from user-bundle, user-item, and bundle-item interactions, deriving corresponding feature representations. However, these approaches often emphasize the distinctions among these three interaction types or treat them uniformly, neglecting the varying importance within one type of interaction and failing to consider the acquisition of information at varying granularities from different types of interactions. In this study, we employ a graph attention mechanism to process user-bundle interaction information, and optimize it using an association enhancement method to extract and construct coarse-grained information representations for users and bundles. By analyzing interactions between users and items, as well as between bundles and items, we identify disparities in item popularity and update the items’ feature representations, facilitating the acquisition of fine-grained information representations for users and bundles. By merging this information, we achieve more comprehensive representations of user intent and bundle characteristics. Extensive experiments on two real-world datasets convincingly demonstrate that our approach significantly advances the task of bundle recommendation, outperforming state-of-the-art methods. |
| format | Article |
| id | doaj-art-663bd44a4fe6484c8cd12e2f5499c9aa |
| institution | DOAJ |
| issn | 2096-0654 2097-406X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-663bd44a4fe6484c8cd12e2f5499c9aa2025-08-20T03:13:36ZengTsinghua University PressBig Data Mining and Analytics2096-06542097-406X2025-05-018375176610.26599/BDMA.2025.9020016IDBR: Interaction-Aware Dual-Granularity Learning for Bundle RecommendationJinqing Wang0Yuan Cao1Fan Zhang2Feifei Kou3Kaimin Wei4Jinghui Zhang5Jinpeng Chen6School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China, and also with Xiangjiang Laboratory, Changsha 410205, ChinaFaculty of Science, National University of Singapore, Singapore 119077, SingaporeSchool of Computer Science (National Pilot Software Engineering School) and Key Laboratory of Trustworthy Distributed Computing and Service of Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Computer Science (National Pilot Software Engineering School) and Key Laboratory of Trustworthy Distributed Computing and Service of Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaCollege of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaXiangjiang Laboratory, Changsha 410205, China, and also with Business School, Central South University, Changsha 410083, ChinaSchool of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China, and also with Xiangjiang Laboratory, Changsha 410205, ChinaIn the recommendation system, bundle recommendation is a prevalent sales strategy in which a combination of diverse, related, or complementary products is suggested to consumers. Recent methodologies frequently utilize graph neural networks to capture information from user-bundle, user-item, and bundle-item interactions, deriving corresponding feature representations. However, these approaches often emphasize the distinctions among these three interaction types or treat them uniformly, neglecting the varying importance within one type of interaction and failing to consider the acquisition of information at varying granularities from different types of interactions. In this study, we employ a graph attention mechanism to process user-bundle interaction information, and optimize it using an association enhancement method to extract and construct coarse-grained information representations for users and bundles. By analyzing interactions between users and items, as well as between bundles and items, we identify disparities in item popularity and update the items’ feature representations, facilitating the acquisition of fine-grained information representations for users and bundles. By merging this information, we achieve more comprehensive representations of user intent and bundle characteristics. Extensive experiments on two real-world datasets convincingly demonstrate that our approach significantly advances the task of bundle recommendation, outperforming state-of-the-art methods.https://www.sciopen.com/article/10.26599/BDMA.2025.9020016recommendation systembundle recommendationgraph attention mechanismcoarse-grained informationfine-grained information |
| spellingShingle | Jinqing Wang Yuan Cao Fan Zhang Feifei Kou Kaimin Wei Jinghui Zhang Jinpeng Chen IDBR: Interaction-Aware Dual-Granularity Learning for Bundle Recommendation Big Data Mining and Analytics recommendation system bundle recommendation graph attention mechanism coarse-grained information fine-grained information |
| title | IDBR: Interaction-Aware Dual-Granularity Learning for Bundle Recommendation |
| title_full | IDBR: Interaction-Aware Dual-Granularity Learning for Bundle Recommendation |
| title_fullStr | IDBR: Interaction-Aware Dual-Granularity Learning for Bundle Recommendation |
| title_full_unstemmed | IDBR: Interaction-Aware Dual-Granularity Learning for Bundle Recommendation |
| title_short | IDBR: Interaction-Aware Dual-Granularity Learning for Bundle Recommendation |
| title_sort | idbr interaction aware dual granularity learning for bundle recommendation |
| topic | recommendation system bundle recommendation graph attention mechanism coarse-grained information fine-grained information |
| url | https://www.sciopen.com/article/10.26599/BDMA.2025.9020016 |
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