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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2025-05-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2025.9020016 |
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| Summary: | 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. |
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| ISSN: | 2096-0654 2097-406X |