Cross-view contrastive representation learning on meta-path induced graphs with node features for bundle...

Bundle recommendation is designed to suggest a set of correlated items to a user in a holistic manner rather than recommending these items separately. Recent methods introduce contrastive learning (CL) to refine the node representations learned from different graphs (generally termed the item and bundle views) for better recommendation performance. Unfortunately, these methods have two deficiencies. Firstly, few of them explicitly model the user-user and bundle-bundle relationships simultaneously from both the item and bundle views, leading to the underutilization of high-order relationships between users (bundles). Secondly, they use InfoNCE as the contrastive loss, which overlooks the graph structure as supervised signals in defining positive (negative) samples, resulting in anchor-like nodes being treated as negative samples. To tackle these deficiencies, an approach of cross-view contrastive representation learning (CCRL) on meta-path induced graphs with node features is proposed for bundle recommendation. First, we introduce meta-path to model the user-user and bundle-bundle relationships as meta-path induced graphs with node features from both the item and bundle views. Second, we perform graph representation learning on the meta-path induced graphs with node features to procure the user (bundle) representations and introduce a contrastive loss that supports multiple positive samples to build a cross-view graph CL mechanism for refining the learned user (bundle) representations. Finally, the model is trained with a joint optimization objective. Experiments on the benchmark datasets manifest that our approach surpasses the baselines in bundle recommendation.