Auto Graph Filtering for Bundle Recommendation

Bundle recommendation focuses on recommending users with associated item sets at once. Recently, some works utilize Graph Neural Network (GNN), which has a solid power for mining information behind the topological structure, to enhance bundle recommendation performance. The previous GNN-based methods focus on designing the bundle-item association mechanism and fusing the extra information from the item view into the final prediction. However, the crucial component in GNN, namely the neighborhood aggregation mechanism, is yet to be explored under the bundle recommendation scenario. In this work, we propose a bundle-specific neighborhood aggregation mechanism named Auto Graph Filtering (AGF). The AGF refines the neighborhood aggregation mechanism from two aspects. (1) AGF utilizes the 2-hop meta paths in the bundle recommendation scenario instead of user interactions directly, which alleviates the extreme sparsity in the user-bundle graph. (2) AGF automatically reweights all the meta paths during the training. With training procedure completed, AGF optimizes the user-bundle graph to meet the bundle recommendation requirement. The experimental results show that our simplest AGF version, AGFN, consistently outperforms all the baselines. Moreover, the user-bundle graph learned by AGFN could also boost the existing GNN-based methods to achieve a better performance.