DMR: disentangled and denoised learning for multi-behavior recommendation
Abstract In recommender systems, leveraging auxiliary behaviors (e.g. view, cart) to enhance the recommendation in the target behavior (e.g. purchase) is crucial for mitigating the sparsity issue inherent in single-behavior recommendation. This has given rise to the multi-behavior recommendation (MB...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01778-5 |
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| author | Yijia Zhang Wanyu Chen Fei Cai Zhenkun Shi Feng Qi |
| author_facet | Yijia Zhang Wanyu Chen Fei Cai Zhenkun Shi Feng Qi |
| author_sort | Yijia Zhang |
| collection | DOAJ |
| description | Abstract In recommender systems, leveraging auxiliary behaviors (e.g. view, cart) to enhance the recommendation in the target behavior (e.g. purchase) is crucial for mitigating the sparsity issue inherent in single-behavior recommendation. This has given rise to the multi-behavior recommendation (MBR). Existing MBR task faces two primary challenges. First, the irrelevant auxiliary behaviors that do not align with the target behavior, can negatively impact the prediction accuracy for user preference in the target behavior. Second, these methods typically learn coarse-grained user preferences, failing to model the consistency and distinctiveness among multiple behaviors at a fine-grained level. To address these issues, we propose a disentangled and denoised model for multi-behavior recommendation (DMR), which employs user preferences reflected in the target behavior to guide the learning of user and item embeddings in auxiliary behaviors. Specifically, we first design a disentangled graph convolutional network, modeling the fine-grained user preference under multiple behaviors in view of item attribute domains. We also propose a denoised contrastive learning strategy, where we align the user preferences in multiple behaviors by reducing the influence of noisy data existing in auxiliary behaviors. Experimental results on two real-world datasets show the proposal can improve the performance of MBR models effectively, which achieves on average 3.12% on the Retailrocket dataset and 3.28% on the Beibei dataset over the performance of state-of-the-art baselines. Extensive experiments also demonstrate our model’s competitive performance for fine-grained preference learning and denoised learning. |
| format | Article |
| id | doaj-art-54e825c4c7524f3b8f953239fbd7111b |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-54e825c4c7524f3b8f953239fbd7111b2025-02-09T13:01:21ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111212310.1007/s40747-024-01778-5DMR: disentangled and denoised learning for multi-behavior recommendationYijia Zhang0Wanyu Chen1Fei Cai2Zhenkun Shi3Feng Qi4College of Electronic Countermeasures, National University of Defense TechnologyCollege of Electronic Countermeasures, National University of Defense TechnologyCollege of Systems Engineering, National University of Defense TechnologyCollege of Electronic Countermeasures, National University of Defense TechnologyTianjin Institute of Industrial Biotechnology, Chinese Academy of SciencesAbstract In recommender systems, leveraging auxiliary behaviors (e.g. view, cart) to enhance the recommendation in the target behavior (e.g. purchase) is crucial for mitigating the sparsity issue inherent in single-behavior recommendation. This has given rise to the multi-behavior recommendation (MBR). Existing MBR task faces two primary challenges. First, the irrelevant auxiliary behaviors that do not align with the target behavior, can negatively impact the prediction accuracy for user preference in the target behavior. Second, these methods typically learn coarse-grained user preferences, failing to model the consistency and distinctiveness among multiple behaviors at a fine-grained level. To address these issues, we propose a disentangled and denoised model for multi-behavior recommendation (DMR), which employs user preferences reflected in the target behavior to guide the learning of user and item embeddings in auxiliary behaviors. Specifically, we first design a disentangled graph convolutional network, modeling the fine-grained user preference under multiple behaviors in view of item attribute domains. We also propose a denoised contrastive learning strategy, where we align the user preferences in multiple behaviors by reducing the influence of noisy data existing in auxiliary behaviors. Experimental results on two real-world datasets show the proposal can improve the performance of MBR models effectively, which achieves on average 3.12% on the Retailrocket dataset and 3.28% on the Beibei dataset over the performance of state-of-the-art baselines. Extensive experiments also demonstrate our model’s competitive performance for fine-grained preference learning and denoised learning.https://doi.org/10.1007/s40747-024-01778-5Multi-behavior recommendationFine-grained preferencesContrastive learningGraph convolutional network |
| spellingShingle | Yijia Zhang Wanyu Chen Fei Cai Zhenkun Shi Feng Qi DMR: disentangled and denoised learning for multi-behavior recommendation Complex & Intelligent Systems Multi-behavior recommendation Fine-grained preferences Contrastive learning Graph convolutional network |
| title | DMR: disentangled and denoised learning for multi-behavior recommendation |
| title_full | DMR: disentangled and denoised learning for multi-behavior recommendation |
| title_fullStr | DMR: disentangled and denoised learning for multi-behavior recommendation |
| title_full_unstemmed | DMR: disentangled and denoised learning for multi-behavior recommendation |
| title_short | DMR: disentangled and denoised learning for multi-behavior recommendation |
| title_sort | dmr disentangled and denoised learning for multi behavior recommendation |
| topic | Multi-behavior recommendation Fine-grained preferences Contrastive learning Graph convolutional network |
| url | https://doi.org/10.1007/s40747-024-01778-5 |
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