Disentangling from Collaborative and Semantic Views: Graph Collaborative Filtering for Q&A Recommendation
sigir26-029 · Full research paper · Changshuo Zhang, Teng Shi, Xiao Zhang, Yanping Zheng, Ruobing Xie, Qi Liu, Jun Xu
Graph Neural Network Architectures f · 50% Graph Learning for Text-Attributed a · 25% Graph-based User and Item Representa · 25%
Abstract
Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs, unlike traditional recommendations that recommend only one item. This makes user behaviors more complex, and presents two challenges for Q&A recommendation, including: the collaborative information entanglement, which means user feedback is influenced by either the question or the answer; and the semantic information entanglement, where questions are correlated with their corresponding answers, and correlations also exist among different question-answer pairs. Traditional recommendation methods treat the question-answer pair as a whole or only consider the answer as a single item, which overlooks the two challenges and cannot effectively model user interests. To address these challenges, we introduce a graph neural network model named Question & Answer Graph Collaborative Filtering (QAGCF). QAGCF creates graphs separately from collaborative and semantic views to disentangle the collaborative and semantic information of question-answer pairs. The collaborative view disentangles questions and answers to individually model collaborative information, while the semantic view captures the semantic information both within and between question-answer pairs. These views are further merged into a global graph to integrate the collaborative and semantic information. Polynomial-based graph filters are used to address the high heterophily issues of the global graph. Additionally, contrastive learning is utilized to obtain robust embeddings during training. Extensive experiments on industrial and public datasets demonstrate that QAGCF consistently outperforms baselines and achieves state-of-the-art results.
Assigned reviewers
| # | Reviewer | Match | Why (closest evidence publications in the taxonomy) | |
|---|---|---|---|---|
| 1 | Jing Zhu 0005 | 61 | 54 TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning (2024) Graph Pretraining and Foundation Models 53 Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding (2021) Graph-based User and Item Representation Learning 53 CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment (2022) Graph-based User and Item Representation Learning |
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| 2 | Dongxiao He | 57 | 56 Analyzing Heterogeneous Networks With Missing Attributes by Unsupervised Contrastive Learning (2024) Graph Contrastive and Self-Supervised Learning 54 DuoKD: Dual Knowledge Distillation from Large Language Models for Robust Graph Neural Networks (2026) Graph Pretraining and Foundation Models 53 LLM-FSGNN: LLM-Guided Feature-Structure Augmentation Graph Neural Network for Cold-Start Recommendation (2026) LLM-Derived Knowledge Infusion |
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| 3 | Xiaolong Liu 0012 | 55 | 86 DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation (2023) Graph-based User and Item Representation Learning 65 Unified Pretraining for Recommendation via Task Hypergraphs (2024) Graph Neural Network Architectures for CF 57 Graph-based Alignment and Uniformity for Recommendation (2023) Graph-based User and Item Representation Learning |
Area chair
| Area chair | Match | |
|---|---|---|
| Qiaoyu Tan | 49 |
Area-chair candidates (6)
| Qiaoyu Tan assigned | 49 | 7 papers | |
| Yankai Chen 0001 | 44 | 6 papers | |
| Xin Xin 0003 COI: co-author | 42 | 10 papers | |
| Leyu Lin COI: co-author | 42 | 15 papers | |
| Yanan Niu COI: co-author | 40 | 15 papers | |
| Maolin Wang 0001 COI: co-author | 39 | 15 papers |
Candidates from the panel ranked by match score
| # | Reviewer | Match | Load | Why | |
|---|---|---|---|---|---|
| 1 | Jing Zhu 0005 assigned | 61 | 3 | 54 TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning (2024) Graph Pretraining and Foundation Models 53 Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding (2021) Graph-based User and Item Representation Learning |
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| 2 | Dongxiao He assigned | 57 | 3 | 56 Analyzing Heterogeneous Networks With Missing Attributes by Unsupervised Contrastive Learning (2024) Graph Contrastive and Self-Supervised Learning 54 DuoKD: Dual Knowledge Distillation from Large Language Models for Robust Graph Neural Networks (2026) Graph Pretraining and Foundation Models |
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| 3 | Xiaolong Liu 0012 assigned | 55 | 3 | 86 DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation (2023) Graph-based User and Item Representation Learning 65 Unified Pretraining for Recommendation via Task Hypergraphs (2024) Graph Neural Network Architectures for CF |
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| 4 | Xurong Liang | 51 | 3 | 60 Lightweight Embeddings for Graph Collaborative Filtering (2024) Graph-based User and Item Representation Learning 60 Lightweight Embeddings with Graph Rewiring for Collaborative Filtering (2025) Graph-based User and Item Representation Learning |
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| 6 | Yongjing Hao | 49 | 3 | 86 How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals (2026) Graph-based User and Item Representation Learning 59 A General Strategy Graph Collaborative Filtering for Recommendation Unlearning (2024) Graph Neural Network Architectures for CF |
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| 7 | Chen Wang 0052 | 48 | 3 | 76 Unified Pretraining for Recommendation via Task Hypergraphs (2024) Graph Pretraining and Foundation Models 57 Graph-based Alignment and Uniformity for Recommendation (2023) Graph-based User and Item Representation Learning |
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| 8 | Hyunsik Yoo | 48 | 3 | 53 Directed Network Embedding with Virtual Negative Edges (2022) Graph-based User and Item Representation Learning 52 Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding (2023) Graph Anomaly and Out-of-Distribution Detection |
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| 9 | Yi Zhang 0103 | 47 | 3 | 76 Revisiting Graph-based Recommender Systems from the Perspective of Variational Auto-Encoder (2023) Graph-based User and Item Representation Learning 59 Intent-Guided Heterogeneous Graph Contrastive Learning for Recommendation (2025) Graph Contrastive and Self-Supervised Learning |
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| 10 | Ke Wang 0038 | 47 | 3 | 69 Learning Aspect-Aware High-Order Representations from Ratings and Reviews for Recommendation (2023) Graph Learning for Text-Attributed and Heterogeneous Networks 59 Inter- and Intra-Domain Relation-Aware Heterogeneous Graph Convolutional Networks for Cross-Domain Recommendation (2022) Graph Contrastive and Self-Supervised Learning |
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| 11 | Xubin Ren | 46 | 3 | 56 Disentangled Contrastive Collaborative Filtering (2023) Graph Contrastive and Self-Supervised Learning 53 How Expressive are Graph Neural Networks in Recommendation? (2023) Graph-based User and Item Representation Learning |
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| 12 | Riwei Lai | 46 | 3 | 69 Multi-Faceted Global Item Relation Learning for Session-Based Recommendation (2022) Graph-Based Sequential and Session Recommendation 50 Disentangled Negative Sampling for Collaborative Filtering (2023) Graph Contrastive and Self-Supervised Learning |
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| 13 | Lianghao Xia | 46 | 3 | 59 Self-Supervised Hypergraph Transformer for Recommender Systems (2022) Graph Contrastive and Self-Supervised Learning 50 Knowledge Graph Self-Supervised Rationalization for Recommendation (2023) Knowledge Graph Embedding and Completion |
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| 14 | Mengyuan Jing | 45 | 3 | 53 Learning Shared Representations for Recommendation with Dynamic Heterogeneous Graph Convolutional Networks (2023) Graph-based User and Item Representation Learning 44 Interest-Aware Graph Contrastive Learning for Recommendation With Diffusion-Based Augmentation (2026) Graph Contrastive and Self-Supervised Learning |
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| 16 | Noveen Sachdeva | 44 | 3 | 60 ECLARE: Extreme Classification with Label Graph Correlations (2021) Graph Neural Network Architectures for CF 53 GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights (2025) Graph-based User and Item Representation Learning |
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| 17 | Mingdai Yang | 44 | 3 | 61 Enhanced Pre-training for Recommendation via Hypergraph Structural Entropy (2026) Graph Pretraining and Foundation Models 61 Unified Pretraining for Recommendation via Task Hypergraphs (2024) Graph Pretraining and Foundation Models |
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| 18 | Jurek Leonhardt | 43 | 3 | 53 Node Representation Learning for Directed Graphs (2019) Graph-based User and Item Representation Learning 33 Extractive Explanations for Interpretable Text Ranking (2023) Cross-Encoder and Neural Reranking |
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| 19 | Jiancan Wu | 42 | 3 | 86 Graph convolution machine for context-aware recommender system (2022) Graph-based User and Item Representation Learning 48 Graph Contrastive Learning With Negative Propagation for Recommendation (2024) Graph Contrastive and Self-Supervised Learning |
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| 20 | Yixin Liu 0001 | 42 | 3 | 53 Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation (2024) Graph-based User and Item Representation Learning 44 SpecG: A Spectral-Based Framework for Effective Graph Pretraining and Knowledge Transfer (2025) Graph Pretraining and Foundation Models |
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| 21 | Lingling Yi | 42 | 3 | 60 PlatoGL: Effective and Scalable Deep Graph Learning System for Graph-enhanced Real-Time Recommendation (2022) Graph-based User and Item Representation Learning 52 Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation (2025) Sequential and Session-Based Graph Recommendation |
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| 24 | Chunyang Wang 0001 | 42 | 3 | 53 Learning Graph-based Disentangled Representations for Next POI Recommendation (2022) Sequential and Session-Based Graph Recommendation 53 Learning Aspect-Aware High-Order Representations from Ratings and Reviews for Recommendation (2023) Graph Learning for Text-Attributed and Heterogeneous Networks |
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| 25 | Jianghong Ma | 41 | 3 | 60 Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation (2023) Graph-based User and Item Representation Learning 55 Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure (2026) Graph-based User and Item Representation Learning |
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| 26 | Wei Wei 0027 | 41 | 3 | 59 Heterogeneous Graph Contrastive Learning for Recommendation (2023) Graph Contrastive and Self-Supervised Learning 52 LLMRec: Large Language Models with Graph Augmentation for Recommendation (2024) Graph Pretraining and Foundation Models |
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| 27 | Jinheon Baek | 40 | 3 | 53 Accurate Learning of Graph Representations with Graph Multiset Pooling (2021) Graph-based User and Item Representation Learning 53 Edge Representation Learning with Hypergraphs (2021) Graph-based User and Item Representation Learning |
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| 28 | Yinwei Wei | 40 | 3 | 49 Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback (2020) Graph Contrastive and Self-Supervised Learning 44 MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation (2024) Graph Contrastive and Self-Supervised Learning |
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| 29 | Tianzhe Zhao | 40 | 3 | 47 Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction (2022) Knowledge Graph Embedding and Completion 47 Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations (2022) Knowledge Graph Embedding and Completion |
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| 31 | Wei Guo 0006 | 39 | 3 | 55 Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation (2023) Graph Contrastive and Self-Supervised Learning 54 Compressed Interaction Graph based Framework for Multi-behavior Recommendation (2023) Sequential and Session-Based Graph Recommendation |
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| 32 | Jin Chen 0008 COI: co-author | 39 | 3 | 42 Cooperative Retriever and Ranker in Deep Recommenders (2023) Recommendation with Search and Ranking 40 Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation (2024) Recommendation Systems and Evaluation |
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| 33 | Jianling Wang | 39 | 3 | 41 Session-based Recommendation with Hypergraph Attention Networks (2021) Sequential and Session-Based Graph Recommendation 41 Next-item Recommendation with Sequential Hypergraphs (2020) Sequential and Session-Based Graph Recommendation |
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| 34 | Jiuqiang Li 0002 | 39 | 3 | 43 Graph Diffusive Self-Supervised Learning for Social Recommendation (2024) Social and Group Recommendation 40 Disentangled Contrastive Bundle Recommendation with Conditional Diffusion (2025) Diffusion and Flow Recommenders |
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| 35 | Seongmin Park 0002 | 39 | 3 | 46 LLM-Enhanced Linear Autoencoders for Recommendation (2025) Collaborative-Semantic Representation Alignment 43 Linear Item-Item Models with Neural Knowledge for Session-based Recommendation (2025) Efficient and Long-Sequence Sequential Recommendation |