Interest transfer graph convolutional networks for multi-behavior recommendation

Multi-behavior recommendation is an effective approach to address the problem of data sparsity. Graph-based multi-behavior recommendation is one of the most promising branches. Most research on multi-behavior recommendation uses Graph Convolutional Networks (GCNs) to model user features, as GCNs can capture high-order relationships and global features between nodes. However, the existing approaches suffer from multi-level user interests and noisy interactions. To address this issue, we propose a novel Interest Transfer Graph Convolutional Networks (ITGCN) for multi-behavior recommendation. Specifically, to model multi-level user interests, we designed a multi-level GCN by removing multi-layer aggregation operations to capture high-order relationships between nodes. In addition, to address the issue of noisy interactions, we propose a multi-behavior interest transfer method. This approach uses similarity-based comparison to reduce the impact of noisy interactions. It makes both target and auxiliary behaviors more robust to noise. At the same time, it transfers interests from auxiliary behaviors into the semantic space of the target behavior. Experiments on four datasets demonstrated the effectiveness of ITGCN.