Same Frequency Begets Shared Interests: Popular-Niche Wavelet Graph Learning for Multimodal Recommendation
sigir26-084 · Full research paper · Yue He, Hongbo Chen, Jingxi Xie, Fengling Li, Jingjing Li
Graph Contrastive and Self-Supervise · 50% Graph Contrastive and Self-Supervise · 25% Multimodal Denoising and Robustness · 25%
Abstract
Multimodal recommendation systems have achieved success in capturing user interests, yet user interests are inherently diverse. Existing GCN-based methods adopt a uniform approach to model user-item interaction graphs, failing to differentiate between ''popular interests'' and ''niche interests''. This deficiency leads to problems such as the information of popular items overshadowing that of niche items during message passing and multimodal fusion, thereby compromising the accuracy and diversity of recommendations. To address these problems, we propose a novel Popular-Niche Graph Wavelet Learning Framework for Multimodal Recommendation (PNGRec). PNGRec first extracts user behavior signals and item popularity signals from the user-item interaction graph, then decomposes the frequency-domain signals of interests into a Popular User-Item Graph and a Niche Graph via frequency-domain signal decomposition, aiming to capture users' popular and niche interests respectively. Furthermore, we introduce a User Interests Four Quadrants Graph Learning mechanism, which enhances the diversity of users' interests under multimodal perception by finely dividing user behavior representations and item popularity representations. Finally, the model is optimized through a self-supervised multi-task joint optimization loss. Extensive experiments on four real-world industrial datasets demonstrate the effectiveness of our proposed interest-divided modeling approach. The source code is publicly available at https://github.com/orangeheyue/PNGRec.
Assigned reviewers
| # | Reviewer | Match | Why (closest evidence publications in the taxonomy) | |
|---|---|---|---|---|
| 1 | Yinwei Wei | 74 | 100 Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation (2025) Graph Contrastive and Self-Supervised Learning 100 Multi-modal Bipartite Graph Structure Learning with Information Bottleneck for Micro-video Recommendation (2026) Graph Contrastive and Self-Supervised Learning 93 Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback (2020) Graph Contrastive and Self-Supervised Learning |
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| 2 | Mengyuan Jing | 60 | 70 Contrastive Self-supervised Learning in Recommender Systems: A Survey (2024) Graph Contrastive and Self-Supervised Learning 70 Review-Enhanced Hierarchical Contrastive Learning for Recommendation (2024) Graph Contrastive and Self-Supervised Learning 70 Rating-Review Graph Contrastive Learning for Review-based Recommendation (2023) Graph Contrastive and Self-Supervised Learning |
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| 3 | Yi Zhang 0103 | 56 | 84 Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering (2025) Graph Contrastive and Self-Supervised Learning 84 MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems (2025) Graph Contrastive and Self-Supervised Learning 69 Intent-Guided Heterogeneous Graph Contrastive Learning for Recommendation (2025) Graph Contrastive and Self-Supervised Learning |
Area chair
| Area chair | Match | |
|---|---|---|
| Leyu Lin | 46 |
Area-chair candidates (4)
| Leyu Lin assigned | 46 | 15 papers | |
| Weiming Liu 0005 | 44 | 8 papers | |
| Qiaoyu Tan | 43 | 7 papers | |
| Kun Gai | 39 | 5 papers |
Candidates from the panel ranked by match score
| # | Reviewer | Match | Load | Why | |
|---|---|---|---|---|---|
| 1 | Yinwei Wei assigned | 74 | 3 | 100 Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation (2025) Graph Contrastive and Self-Supervised Learning 100 Multi-modal Bipartite Graph Structure Learning with Information Bottleneck for Micro-video Recommendation (2026) Graph Contrastive and Self-Supervised Learning |
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| 2 | Lianghao Xia | 62 | 3 | 84 Automated Self-Supervised Learning for Recommendation (2023) Graph Contrastive and Self-Supervised Learning 84 LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation (2023) Graph Contrastive and Self-Supervised Learning |
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| 3 | Ke Wang 0038 | 60 | 3 | 85 Review-Enhanced Hierarchical Contrastive Learning for Recommendation (2024) Graph Contrastive and Self-Supervised Learning 85 Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation (2024) Graph Contrastive and Self-Supervised Learning |
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| 4 | Jiuqiang Li 0002 | 60 | 3 | 62 Multi-view Semantic Contrastive Alignment for Multimodal Recommendation (2026) Universal Multimodal Embedding and MLLM Retrieval 59 Graph Diffusive Self-Supervised Learning for Social Recommendation (2024) Social and Group Recommendation |
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| 5 | Mengyuan Jing assigned | 60 | 3 | 70 Contrastive Self-supervised Learning in Recommender Systems: A Survey (2024) Graph Contrastive and Self-Supervised Learning 70 Review-Enhanced Hierarchical Contrastive Learning for Recommendation (2024) Graph Contrastive and Self-Supervised Learning |
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| 6 | Yi Zhang 0103 assigned | 56 | 3 | 84 Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering (2025) Graph Contrastive and Self-Supervised Learning 84 MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems (2025) Graph Contrastive and Self-Supervised Learning |
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| 7 | Xubin Ren | 55 | 3 | 70 SSLRec: A Self-Supervised Learning Framework for Recommendation (2024) Graph Contrastive and Self-Supervised Learning 70 LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation (2023) Graph Contrastive and Self-Supervised Learning |
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| 8 | Dongxiao He | 52 | 3 | 70 Contrastive Learning Meets Homophily: Two Birds with One Stone (2023) Graph Contrastive and Self-Supervised Learning 70 Self-Supervised Graph Neural Networks via Diverse and Interactive Message Passing (2022) Graph Contrastive and Self-Supervised Learning |
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| 9 | Wei Wei 0027 | 52 | 3 | 69 Multi-Modal Self-Supervised Learning for Recommendation (2023) Graph Contrastive and Self-Supervised Learning 69 Heterogeneous Graph Contrastive Learning for Recommendation (2023) Graph Contrastive and Self-Supervised Learning |
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| 10 | Chunyang Wang 0001 | 51 | 3 | 100 Guiding Graph Learning with Denoised Modality for Multi-modal Recommendation (2024) Graph Contrastive and Self-Supervised Learning 85 Review-Enhanced Hierarchical Contrastive Learning for Recommendation (2024) Graph Contrastive and Self-Supervised Learning |
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| 11 | Xin Zhang 0091 | 48 | 3 | 48 Multi-Modal Correction Network for Recommendation (2025) Multimodal Denoising and Robustness 43 Deep Session Heterogeneity-Aware Network for Click Through Rate Prediction (2024) CTR and Conversion Rate Prediction |
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| 12 | Yongjing Hao | 46 | 3 | 83 Meta-optimized Structural and Semantic Contrastive Learning for Graph Collaborative Filtering (2024) Graph Contrastive and Self-Supervised Learning 83 LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive Learning (2024) Graph Contrastive and Self-Supervised Learning |
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| 14 | Miaomiao Cai 0001 | 45 | 3 | 54 I3-MRec: Invariant Learning with Information Bottleneck for Incomplete Modality Recommendation (2025) Multimodal Denoising and Robustness 54 Multimodality Invariant Learning for Multimedia-Based New Item Recommendation (2024) Content and Attribute-Based Cold-Start Recommendation |
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| 15 | Xiaolong Liu 0012 | 45 | 3 | 60 Graph-based Alignment and Uniformity for Recommendation (2023) Graph-based User and Item Representation Learning 59 Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning (2023) Social and Group Recommendation |
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| 16 | Lantao Hu | 45 | 3 | 70 Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning (2024) Graph Contrastive and Self-Supervised Learning 58 Soft Contrastive Sequential Recommendation (2024) Contrastive and Self-Supervised Sequential Recommendation |
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| 17 | Hao Deng 0011 | 45 | 3 | 50 ESANS: Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems (2025) Multimodal Denoising and Robustness 44 MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation (2026) Item Tokenization and Semantic IDs |
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| 19 | Bowen Zheng 0005 | 44 | 3 | 84 Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation (2025) Graph Contrastive and Self-Supervised Learning 43 Generative Recommender with End-to-End Learnable Item Tokenization (2025) Item Tokenization and Semantic IDs |
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| 20 | Chen Wang 0052 | 43 | 3 | 60 Graph-based Alignment and Uniformity for Recommendation (2023) Graph-based User and Item Representation Learning 48 Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning (2023) Social and Group Recommendation |
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| 21 | Jiancan Wu | 43 | 3 | 84 Self-supervised Graph Learning for Recommendation (2021) Graph Contrastive and Self-Supervised Learning 82 Graph Contrastive Learning With Negative Propagation for Recommendation (2024) Graph Contrastive and Self-Supervised Learning |
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| 23 | Yong He 0009 | 43 | 3 | 60 Global-Aware Model-Free Self-distillation for Recommendation System (2023) Graph Contrastive and Self-Supervised Learning 44 The Devil is in the Sources! Knowledge Enhanced Cross-Domain Recommendation in an Information Bottleneck Perspective (2024) Domain Adaptation and Disentangled Representation for CDR |
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| 24 | Min Hou 0004 | 43 | 3 | 54 Unified Representation Learning for Discrete Attribute Enhanced Completely Cold-Start Recommendation (2025) Graph Contrastive and Self-Supervised Learning 54 Multimodality Invariant Learning for Multimedia-Based New Item Recommendation (2024) Content and Attribute-Based Cold-Start Recommendation |
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| 25 | Yixin Liu 0001 | 42 | 3 | 70 Learning Strong Graph Neural Networks with Weak Information (2023) Graph Contrastive and Self-Supervised Learning 70 Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating (2023) Graph Contrastive and Self-Supervised Learning |
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| 26 | Jiangxia Cao | 42 | 3 | 58 QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou (2025) Multimodal Generative Recommendation 44 DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation (2022) Domain Adaptation and Disentangled Representation for CDR |
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| 27 | Mingdai Yang | 42 | 3 | 60 Graph-based Alignment and Uniformity for Recommendation (2023) Graph-based User and Item Representation Learning 59 Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning (2023) Social and Group Recommendation |
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| 28 | Lingling Yi | 42 | 3 | 72 Dual Interests-Aligned Graph Auto-Encoders for Cross-domain Recommendation in WeChat (2023) Graph Contrastive and Self-Supervised Learning 67 Item-Provider Co-learning for Sequential Recommendation (2022) Graph Contrastive and Self-Supervised Learning |
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| 29 | Lichan Hong | 41 | 3 | 60 Self-supervised Learning for Large-scale Item Recommendations (2021) Graph Contrastive and Self-Supervised Learning 44 Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations (2024) Item Tokenization and Semantic IDs |
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| 30 | Yongxin Ni | 41 | 3 | 55 Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration (2025) Multimodal Generative Recommendation 46 Causality-Inspired Fair Representation Learning for Multimodal Recommendation (2025) Bias Mitigation Methods |
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| 31 | Riwei Lai | 41 | 3 | 53 Disentangled Negative Sampling for Collaborative Filtering (2023) Graph Contrastive and Self-Supervised Learning 40 DAR: Dimension-Adaptive Recommendation with Multi-Granular Noise Control (2025) Robustness to Noise and Distribution Shift |
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| 32 | Jinxin Hu | 41 | 3 | 59 Distribution-Guided Auto-Encoder for User Multimodal Interest Cross Fusion (2025) Content-Based Transferable Recommendation 50 ESANS: Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems (2025) Multimodal Denoising and Robustness |
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| 33 | Ed H. Chi | 41 | 3 | 60 Self-supervised Learning for Large-scale Item Recommendations (2021) Graph Contrastive and Self-Supervised Learning 44 Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations (2024) Item Tokenization and Semantic IDs |