# Learning to Hash with Graph Neural Networks for Recommender Systems

arXiv: https://arxiv.org/abs/2003.01917 · HTML: https://mlsanity.com/paper/2003.01917/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 30 of 31)

- [Interpreting and Unifying Graph Neural Networks with An Optimization Framework](https://mlsanity.com/paper/2101.11859/) (2021)
- [CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification](https://mlsanity.com/paper/2306.04979/) (2023)
- [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021)
- [Toward a Foundation Model for Time Series Data](https://mlsanity.com/paper/2310.03916/) (2023)
- [Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection](https://mlsanity.com/paper/2212.12488/) (2022)
- [Learning by Transference: Training Graph Neural Networks on Growing Graphs](https://mlsanity.com/paper/2106.03693/) (2021)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Efficient Graph Deep Learning in TensorFlow with tf_geometric](https://mlsanity.com/paper/2101.11552/) (2021)
- [Dynamic Memory based Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09269/) (2021)
- [SelfCF: A Simple Framework for Self-supervised Collaborative Filtering](https://mlsanity.com/paper/2107.03019/) (2021)
- [Hashing-Accelerated Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2105.14280/) (2021)
- [Graph Contrastive Learning with Personalized Augmentation](https://mlsanity.com/paper/2209.06560/) (2022)
- [Continual Learning on Graphs: A Survey](https://mlsanity.com/paper/2402.06330/) (2024)
- [Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph](https://mlsanity.com/paper/2208.05648/) (2022)
- [Clustered Embedding Learning for Recommender Systems](https://mlsanity.com/paper/2302.01478/) (2023)
- [Collaborative Graph Neural Networks for Attributed Network Embedding](https://mlsanity.com/paper/2307.11981/) (2023)
- [Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation](https://mlsanity.com/paper/2106.03415/) (2021)
- [Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning](https://mlsanity.com/paper/2408.07985/) (2024)
- [Training Graph Neural Networks on Growing Stochastic Graphs](https://mlsanity.com/paper/2210.15567/) (2022)
- [Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://mlsanity.com/paper/2305.01868/) (2023)
- [CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems](https://mlsanity.com/paper/2506.19993/) (2025)
- [DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research](https://mlsanity.com/paper/2309.01808/) (2023)
- [TeKo: Text-Rich Graph Neural Networks with External Knowledge](https://mlsanity.com/paper/2206.07253/) (2022)
- [WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data](https://mlsanity.com/paper/2312.14129/) (2023)
- [Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning](https://mlsanity.com/paper/2112.01110/) (2021)
- [Learning Similarity Preserving Binary Codes for Recommender Systems](https://mlsanity.com/paper/2204.08569/) (2022)
- [Patient Clustering via Integrated Profiling of Clinical and Digital Data](https://mlsanity.com/paper/2308.11748/) (2023)
- [Inferring Properties of Graph Neural Networks](https://mlsanity.com/paper/2401.03790/) (2024)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [Sparse Decomposition of Graph Neural Networks](https://mlsanity.com/paper/2410.19723/) (2024)

## Abstract (arXiv)

Graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects in the user-item interaction network, the computational costs to infer users' preferences in continuous embedding space are tremendous. In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes. Specifically, a deep hashing with GNNs (HashGNN) is presented, which consists of two components, a GNN encoder for learning node representations, and a hash layer for encoding representations to hash codes. The whole architecture is trained end-to-end by jointly optimizing two losses, i.e., reconstruction loss from reconstructing observed links, and ranking loss from preserving the relative ordering of hash codes. A novel discrete optimization strategy based on straight through estimator (STE) with guidance is proposed. The principal idea is to avoid gradient magnification in back-propagation of STE with continuous embedding guidance, in which we begin from learning an easier network that mimic the continuous embedding and let it evolve during the training until it finally goes back to STE. Comprehensive experiments over three publicly available and one real-world Alibaba company datasets demonstrate that our model not only can achieve comparable performance compared with its continuous counterpart but also runs multiple times faster during inference.