Apr 8, 2022 · Multi-behavior recommendation learns accurate embeddings of users and items with multiple types of interactions.
Multi-behavior recommendation learns accurate embeddings of users and items with multiple types of interactions. Although existing multi-behavior ...
Feb 9, 2023 · First, we utilize node-level attention to learn the representation of users and items under specific behavior. Second, behavioral-level ...
MBGCN [2]: This approach proposes a multi-behavior graph convolutional network-based model that learns behavior intensity through the user–goods propagation ...
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A novel recommendation model DGAMR is proposed, which accurately learns user and item representation by multiple types of behaviors, and utilizes the static ...
Dual graph attention networks for multi-behavior recommendation ... recommendation via two-level graph attentional networks. ... graph convolutional networks. In ...
May 3, 2024 · We propose a multi-behavior recommendation method (MBGR) a graph neural network, that leverages meta-learning models and graph neural network ...
Mar 28, 2023 · For example, DIPN [8] applies a hierarchical attention network to model the relationships between different behaviors in embedding learning ...
To address the issue that the existing multi-behavior recommendation systems do not effectively use di-fferent level graphs to spread information, ...
To solve the above problems, we propose a novel multi-behavior model with relation-aware graph attention network (RGAN), which is built on a graph-based neural ...