DMR: disentangled and denoised learning for multi-behavior recommendation

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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Bibliographic Details
Main Authors: Yijia Zhang, Wanyu Chen, Fei Cai, Zhenkun Shi, Feng Qi
Format: Article
Language:English
Published: Springer 2025-01-01
Series:Complex & Intelligent Systems
Subjects:
Multi-behavior recommendation
Fine-grained preferences
Contrastive learning
Graph convolutional network
Online Access:https://doi.org/10.1007/s40747-024-01778-5

Internet

https://doi.org/10.1007/s40747-024-01778-5

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