Bundle Recommendation

Bundle recommendation aims to suggest sets of related items to users, enhancing user experience and business profitability. Current research focuses on improving the accuracy and efficiency of bundle generation and matching through various techniques, including graph neural networks, multi-view learning, and non-autoregressive models, often incorporating contrastive learning to refine representations. These advancements address challenges like cold-start problems and data sparsity, leading to more effective and personalized recommendations in e-commerce and other applications. The resulting improvements in recommendation accuracy and user engagement demonstrate the significant practical impact of this field.