# BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network

arXiv: https://arxiv.org/abs/2001.09900 · HTML: https://mlsanity.com/paper/2001.09900/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 16 of 16)

- [Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer](https://mlsanity.com/paper/2108.06625/) (2021)
- [Contrastive Self-supervised Sequential Recommendation with Robust Augmentation](https://mlsanity.com/paper/2108.06479/) (2021)
- [Federated Social Recommendation with Graph Neural Network](https://mlsanity.com/paper/2111.10778/) (2021)
- [Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer](https://mlsanity.com/paper/2105.00522/) (2021)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation](https://mlsanity.com/paper/2212.12738/) (2022)
- [Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation](https://mlsanity.com/paper/2106.06165/) (2021)
- [Multi-view Intent Disentangle Graph Networks for Bundle Recommendation](https://mlsanity.com/paper/2202.11425/) (2022)
- [DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN](https://mlsanity.com/paper/2108.11883/) (2021)
- [Pre-training Graph Neural Network for Cross Domain Recommendation](https://mlsanity.com/paper/2111.08268/) (2021)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature](https://mlsanity.com/paper/2205.12784/) (2022)
- [Modeling Dynamic Attributes for Next Basket Recommendation](https://mlsanity.com/paper/2109.11654/) (2021)
- [FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks](https://mlsanity.com/paper/2104.07145/) (2021)
- [Relational Graph Neural Network Design via Progressive Neural Architecture Search](https://mlsanity.com/paper/2105.14490/) (2021)
- [Within-basket Recommendation via Neural Pattern Associator](https://mlsanity.com/paper/2401.16433/) (2024)

## Abstract (arXiv)

Within-basket recommendation reduces the exploration time of users, where the user's intention of the basket matters. The intent of a shopping basket can be retrieved from both user-item collaborative filtering signals and multi-item correlations. By defining a basket entity to represent the basket intent, we can model this problem as a basket-item link prediction task in the User-Basket-Item~(UBI) graph. Previous work solves the problem by leveraging user-item interactions and item-item interactions simultaneously. However, collectivity and heterogeneity characteristics are hardly investigated before. Collectivity defines the semantics of each node which should be aggregated from both directly and indirectly connected neighbors. Heterogeneity comes from multi-type interactions as well as multi-type nodes in the UBI graph. To this end, we propose a new framework named BasConv, which is based on the graph convolutional neural network. Our BasConv model has three types of aggregators specifically designed for three types of nodes. They collectively learn node embeddings from both neighborhood and high-order context. Additionally, the interactive layers in the aggregators can distinguish different types of interactions. Extensive experiments on two real-world datasets prove the effectiveness of BasConv. Our code is available online at https://github.com/JimLiu96/basConv.