Traditional recommendation models primarily rely on display feedback and typically utilize a single type of user-item interaction data, which often results in significant data sparsity issues. In contrast, multi-behavioral recommendation models leverage various behaviors such as browsing, favoriting, and other interactions. These additional behaviors help improve the prediction of user-item interactions. Existing multi-behavioral recommendation methods often overlook the potential factors influencing multi-behavioral interactions and the differences between various behavior types. In this study, we introduce a multi-behavioral recommendation algorithm utilizing decoupled graph convolution (MBR-DGC), which effectively mitigates the data sparsity of the target behaviors and improves recommender system performance by capturing the differences between the semantics of different behaviors. Specifically, we construct multiple non-overlapping independent isomorphic graphs and separate potential factors affecting the interactions among users, items, and behaviors using decoupled convolutional networks to reconstruct the node features of users in different behaviors. Afterwards, multi-behavioral features of users are aggregated using contrastive learning to achieve personalized multi-behavioral information aggregation. Experimental results on multiple datasets show that MBR-DGC effectively leverages multi-behavioral data, significantly enhancing recommendation performance compared to other state-of-the-art methods.
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