Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation

Abstract

Bundle recommendation aims to recommend a bundle of items to users as a whole with user-bundle (U-B) interaction information, and auxiliary user-item (U-I) interaction and bundle-item affiliation information. Recent methods usually use two graph neural networks (GNNs) to model user's bundle preferences separately from the U-B graph (bundle view) and U-I graph (item view). However, by conducting statistical analysis, we find that the auxiliary U-I information is far underexplored due to the following reasons: 1) Loosely combining the predicted results cannot well synthesize the knowledge from both views. 2) The local U-B and U-I collaborative relations might not be consistent, leading to GNN's inaccurate modeling of user's bundle preference from the U-I graph. 3) The U-I interactions are usually modeled equally while the significant ones corresponding to user's bundle preference are less emphasized.