Chunk-Wise Quantization for Graph Collaborative Filtering
sigir26-109 · Full research paper · Kaixi Hu, Peipei Wang, Kaize Shi, Jingling Yuan, Yu Yang, Guandong Xu, Lin Li
Graph-based User and Item Representa · 50% Efficient and Lightweight Graph Coll · 25% Embedding Compression and Quantizati · 25%
Abstract
Energy efficiency has become a critical requirement, driving recommendation systems for resource-constrained environments such as edge devices. Model quantization offers an effective way to build low-bitwidth models while preserving accuracy. However, user–item interaction graphs contain numerous nodes and complex topological structures, leading nodes to exhibit unique similarities and differences. Existing quantization methods uniformly process parameters in high-dimensional DNN layers (e.g., linear, convolutional, or attention layers), while inadequately capturing such similarities among node embeddings. This paper proposes GraphQ, a chunk-wise quantization framework for graph collaborative filtering that supports both the training and post-training phases in a unified perspective. Our core idea is to adaptively partition node embeddings into multiple chunks based on the distribution of embedding values, and then apply chunk-wise quantization. Specifically, for quantization-aware training (QAT), we introduce learnable low-precision quantization factors that partition node embeddings into multiple chunks and are dynamically updated following message passing. For post-training quantization (PTQ), we first cluster nodes and then partition their dimensions into chunks for weight clipping. Extensive experiments on four real-world datasets show that GraphQ outperforms state-of-the-art QAT methods by an average of 27.49% in Recall@10 under the 256-dimensional embedding and 2-bit settings, and surpasses PTQ methods by 78.64% on average under 4-bit settings.
Assigned reviewers
| # | Reviewer | Match | Why (closest evidence publications in the taxonomy) | |
|---|---|---|---|---|
| 1 | Xurong Liang | 70 | 100 Lightweight Embeddings with Graph Rewiring for Collaborative Filtering (2025) Graph-based User and Item Representation Learning 100 Lightweight Embeddings for Graph Collaborative Filtering (2024) Graph-based User and Item Representation Learning 68 Erratum: Lightweight Embeddings with Graph Rewiring for Collaborative Filtering (2026) Recommender Systems › Others |
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| 2 | Jianghong Ma | 44 | 100 Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation (2023) Graph-based User and Item Representation Learning 65 Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure (2026) Graph-based User and Item Representation Learning 63 ExpGCN: Review-aware Graph Convolution Network for explainable recommendation (2023) Graph-based User and Item Representation Learning |
Area chair
| Area chair | Match | |
|---|---|---|
| Yankai Chen 0001 | 46 |
Area-chair candidates (5)
| Yankai Chen 0001 assigned | 46 | 6 papers | |
| Qiaoyu Tan | 44 | 7 papers | |
| Maolin Wang 0001 COI: co-author | 43 | 15 papers | |
| Kun Gai | 40 | 5 papers | |
| Weiming Liu 0005 | 39 | 8 papers |
Candidates from the panel ranked by match score
| # | Reviewer | Match | Load | Why | |
|---|---|---|---|---|---|
| 1 | Xurong Liang assigned | 70 | 3 | 100 Lightweight Embeddings with Graph Rewiring for Collaborative Filtering (2025) Graph-based User and Item Representation Learning 100 Lightweight Embeddings for Graph Collaborative Filtering (2024) Graph-based User and Item Representation Learning |
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| 2 | Hung Vinh Tran | 48 | 3 | 55 On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective (2025) Content-Based Transferable Recommendation 51 A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems (2025) Recommendation Systems and Evaluation |
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| 3 | Xubin Ren | 47 | 3 | 67 Disentangled Contrastive Collaborative Filtering (2023) Graph Contrastive and Self-Supervised Learning 63 How Expressive are Graph Neural Networks in Recommendation? (2023) Graph-based User and Item Representation Learning |
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| 4 | Jing Zhu 0005 | 47 | 3 | 63 Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding (2021) Graph-based User and Item Representation Learning 63 CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment (2022) Graph-based User and Item Representation Learning |
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| 5 | Noveen Sachdeva | 47 | 3 | 63 GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights (2025) Graph-based User and Item Representation Learning 54 ECLARE: Extreme Classification with Label Graph Correlations (2021) Graph Neural Network Architectures for CF |
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| 6 | Xiaolong Liu 0012 | 47 | 3 | 74 DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation (2023) Graph-based User and Item Representation Learning 67 Graph-based Alignment and Uniformity for Recommendation (2023) Graph-based User and Item Representation Learning |
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| 7 | Yi Zhang 0103 | 46 | 3 | 84 Revisiting Graph-based Recommender Systems from the Perspective of Variational Auto-Encoder (2023) Graph-based User and Item Representation Learning 65 Simplify to the Limit! Embedding-Less Graph Collaborative Filtering for Recommender Systems (2025) Graph Contrastive and Self-Supervised Learning |
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| 9 | Hyunsik Yoo | 45 | 3 | 64 Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding (2023) Graph Anomaly and Out-of-Distribution Detection 63 Directed Network Embedding with Virtual Negative Edges (2022) Graph-based User and Item Representation Learning |
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| 10 | Jianghong Ma assigned | 44 | 3 | 100 Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation (2023) Graph-based User and Item Representation Learning 65 Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure (2026) Graph-based User and Item Representation Learning |
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| 11 | Mengyuan Jing | 44 | 3 | 63 Learning Shared Representations for Recommendation with Dynamic Heterogeneous Graph Convolutional Networks (2023) Graph-based User and Item Representation Learning 43 Graph Contrastive Learning with Adaptive Augmentation for Recommendation (2022) Graph Contrastive and Self-Supervised Learning |
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| 12 | Jurek Leonhardt | 44 | 3 | 63 Node Representation Learning for Directed Graphs (2019) Graph-based User and Item Representation Learning 34 Efficient Neural Ranking using Forward Indexes (2022) Learned Sparse Retrieval Efficiency |
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| 13 | Lingling Yi | 44 | 3 | 86 PlatoGL: Effective and Scalable Deep Graph Learning System for Graph-enhanced Real-Time Recommendation (2022) Graph-based User and Item Representation Learning 49 Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering (2023) Efficient and Lightweight Graph Collaborative Filtering |
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| 15 | Jin Chen 0008 | 43 | 3 | 63 Automated Sparse and Low-Rank Shallow Autoencoders for Recommendation (2025) Efficient and Lightweight Graph Collaborative Filtering 63 AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation (2023) Efficient and Lightweight Graph Collaborative Filtering |
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| 17 | Chen Wang 0052 | 43 | 3 | 67 Graph-based Alignment and Uniformity for Recommendation (2023) Graph-based User and Item Representation Learning 50 Unified Pretraining for Recommendation via Task Hypergraphs (2024) Graph Pretraining and Foundation Models |
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| 18 | Lianghao Xia | 43 | 3 | 49 Self-Supervised Hypergraph Transformer for Recommender Systems (2022) Graph Contrastive and Self-Supervised Learning 44 LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation (2023) Graph Contrastive and Self-Supervised Learning |
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| 19 | Mingdai Yang | 43 | 3 | 67 Graph-based Alignment and Uniformity for Recommendation (2023) Graph-based User and Item Representation Learning 48 Enhanced Pre-training for Recommendation via Hypergraph Structural Entropy (2026) Graph Pretraining and Foundation Models |
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| 20 | Gyuseok Lee | 43 | 3 | 49 Continual Collaborative Distillation for Recommender System (2024) Efficient and Lightweight Graph Collaborative Filtering 38 Capturing User Interests from Data Streams for Continual Sequential Recommendation (2026) Efficient Sequential Recommendation Architectures |
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| 21 | Yongjing Hao | 42 | 3 | 74 How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals (2026) Graph-based User and Item Representation Learning 49 LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive Learning (2024) Graph Contrastive and Self-Supervised Learning |
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| 22 | Hao Deng 0011 | 42 | 3 | 52 CSMF: Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval (2025) Multi-Task and Multi-Objective Optimization 39 HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation (2025) Item Tokenization and Semantic IDs |
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| 23 | Yinwei Wei | 42 | 3 | 47 Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback (2020) Graph Contrastive and Self-Supervised Learning 45 MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation (2024) Graph Contrastive and Self-Supervised Learning |
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| 24 | Ke Wang 0038 | 41 | 3 | 44 Disentangled Contrastive Learning for Cross-Domain Recommendation (2023) Graph Contrastive and Self-Supervised Learning 44 Contrastive Self-supervised Learning in Recommender Systems: A Survey (2024) Graph Contrastive and Self-Supervised Learning |
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| 25 | Seongmin Park 0002 | 41 | 3 | 55 LLM-Enhanced Linear Autoencoders for Recommendation (2025) Collaborative-Semantic Representation Alignment 53 Linear Item-Item Models with Neural Knowledge for Session-based Recommendation (2025) Efficient and Long-Sequence Sequential Recommendation |
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| 26 | Jiuqiang Li 0002 | 40 | 3 | 41 Graph Diffusive Self-Supervised Learning for Social Recommendation (2024) Social and Group Recommendation 38 Disentangled Contrastive Bundle Recommendation with Conditional Diffusion (2025) Diffusion and Flow Recommenders |
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| 27 | Jieming Zhu | 40 | 3 | 49 TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems (2025) CTR and Conversion Rate Prediction 39 EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration (2025) Generative Recommendation and Item Tokenization |
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| 28 | Yixin Liu 0001 | 40 | 3 | 63 Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation (2024) Graph-based User and Item Representation Learning 43 Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating (2023) Graph Contrastive and Self-Supervised Learning |
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| 29 | Qijiong Liu | 40 | 3 | 54 Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization (2024) Item Tokenization and Semantic IDs 39 Accelerating Generative Recommendation via Simple Categorical User Sequence Compression (2026) Generative Retrieval and Recommendation Architectures |
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| 31 | Wei Wei 0027 | 39 | 3 | 50 LLMRec: Large Language Models with Graph Augmentation for Recommendation (2024) Graph Pretraining and Foundation Models 45 SSLRec: A Self-Supervised Learning Framework for Recommendation (2024) Recommendation Systems and Evaluation |
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| 32 | Han Li 0005 | 39 | 3 | 63 Learning Tree-based Deep Model for Recommender Systems (2018) Efficient and Lightweight Graph Collaborative Filtering 38 LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application (2025) Efficiency and Deployment |
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| 33 | Dongxiao He | 39 | 3 | 43 Self-Supervised Graph Neural Networks via Diverse and Interactive Message Passing (2022) Graph Contrastive and Self-Supervised Learning 43 Contrastive Learning Meets Homophily: Two Birds with One Stone (2023) Graph Contrastive and Self-Supervised Learning |
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| 34 | Jiancan Wu | 39 | 3 | 74 Graph convolution machine for context-aware recommender system (2022) Graph-based User and Item Representation Learning 47 Graph Contrastive Learning With Negative Propagation for Recommendation (2024) Graph Contrastive and Self-Supervised Learning |