# Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation

arXiv: https://arxiv.org/abs/2307.13468 · HTML: https://mlsanity.com/paper/2307.13468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 22 of 22)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking](https://mlsanity.com/paper/1707.03815/) (2017)
- [Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2109.14545/) (2021)
- [Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning](https://mlsanity.com/paper/2202.06200/) (2022)
- [Contrastive Learning for Cold-Start Recommendation](https://mlsanity.com/paper/2107.05315/) (2021)
- [POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion](https://mlsanity.com/paper/1905.01866/) (2019)
- [Prototypical Graph Contrastive Learning](https://mlsanity.com/paper/2106.09645/) (2021)
- [GraphCL: Contrastive Self-Supervised Learning of Graph Representations](https://mlsanity.com/paper/2007.08025/) (2020)
- [CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation](https://mlsanity.com/paper/2206.00242/) (2022)
- [Bundle Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2005.03475/) (2020)
- [X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning](https://mlsanity.com/paper/2109.03560/) (2021)
- [Enhancing Sequential Recommendation with Graph Contrastive Learning](https://mlsanity.com/paper/2205.14837/) (2022)
- [Personalized Bundle Recommendation in Online Games](https://mlsanity.com/paper/2104.05307/) (2021)
- [Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach](https://mlsanity.com/paper/2008.11567/) (2020)
- [Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty](https://mlsanity.com/paper/2006.10932/) (2020)
- [Gaussian Embedding of Large-scale Attributed Graphs](https://mlsanity.com/paper/1912.00536/) (2019)

## Abstract (arXiv)

Bundle recommendation aims to provide a bundle of items to satisfy the user preference on e-commerce platform. Existing successful solutions are based on the contrastive graph learning paradigm where graph neural networks (GNNs) are employed to learn representations from user-level and bundle-level graph views with a contrastive learning module to enhance the cooperative association between different views. Nevertheless, they ignore the uncertainty issue which has a significant impact in real bundle recommendation scenarios due to the lack of discriminative information caused by highly sparsity or diversity. We further suggest that their instancewise contrastive learning fails to distinguish the semantically similar negatives (i.e., sampling bias issue), resulting in performance degradation. In this paper, we propose a novel Gaussian Graph with Prototypical Contrastive Learning (GPCL) framework to overcome these challenges. In particular, GPCL embeds each user/bundle/item as a Gaussian distribution rather than a fixed vector. We further design a prototypical contrastive learning module to capture the contextual information and mitigate the sampling bias issue. Extensive experiments demonstrate that benefiting from the proposed components, we achieve new state-of-the-art performance compared to previous methods on several public datasets. Moreover, GPCL has been deployed on real-world e-commerce platform and achieved substantial improvements.