# Interest-aware Message-Passing GCN for Recommendation

arXiv: https://arxiv.org/abs/2102.10044 · HTML: https://mlsanity.com/paper/2102.10044/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2021.

## Built on by (most cited first, 30 of 39)

- [LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2302.08191/) (2023)
- [OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval](https://mlsanity.com/paper/2507.05631/) (2025)
- [Blurring-Sharpening Process Models for Collaborative Filtering](https://mlsanity.com/paper/2211.09324/) (2022)
- [HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval](https://mlsanity.com/paper/2512.02792/) (2025)
- [HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval](https://mlsanity.com/paper/2604.18037/) (2026)
- [RecDiff: Diffusion Model for Social Recommendation](https://mlsanity.com/paper/2406.01629/) (2024)
- [A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA](https://mlsanity.com/paper/2206.14989/) (2022)
- [Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis](https://mlsanity.com/paper/2207.11652/) (2022)
- [CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG](https://mlsanity.com/paper/2506.02544/) (2025)
- [HINT: Composed Image Retrieval with Dual-path Compositional Contextualized Network](https://mlsanity.com/paper/2603.26341/) (2026)
- [MELT: Improve Composed Image Retrieval via the Modification Frequentation-Rarity Balance Network](https://mlsanity.com/paper/2603.29291/) (2026)
- [Causal Incremental Graph Convolution for Recommender System Retraining](https://mlsanity.com/paper/2108.06889/) (2021)
- [RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph](https://mlsanity.com/paper/2202.06129/) (2022)
- [Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns](https://mlsanity.com/paper/2312.16199/) (2023)
- [GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment](https://mlsanity.com/paper/2204.01303/) (2022)
- [GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation](https://mlsanity.com/paper/2407.12338/) (2024)
- [Multi-domain Recommendation with Embedding Disentangling and Domain Alignment](https://mlsanity.com/paper/2308.05508/) (2023)
- [Graph Spectral Filtering with Chebyshev Interpolation for Recommendation](https://mlsanity.com/paper/2505.00552/) (2025)
- [Dual Semantic Knowledge Composed Multimodal Dialog Systems](https://mlsanity.com/paper/2305.09990/) (2023)
- [Do Vision-Language Transformers Exhibit Visual Commonsense? An Empirical Study of VCR](https://mlsanity.com/paper/2405.16934/) (2024)
- [Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation](https://mlsanity.com/paper/2501.07110/) (2025)
- [Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation](https://mlsanity.com/paper/2207.05959/) (2022)
- [Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis](https://mlsanity.com/paper/2308.00404/) (2023)
- [Visual Self-paced Iterative Learning for Unsupervised Temporal Action Localization](https://mlsanity.com/paper/2312.07384/) (2023)
- [RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2312.16563/) (2023)
- [Frequency-aware Graph Signal Processing for Collaborative Filtering](https://mlsanity.com/paper/2402.08426/) (2024)
- [G3SR: Global Graph Guided Session-based Recommendation](https://mlsanity.com/paper/2203.06467/) (2022)
- [QoS-Aware Graph Contrastive Learning for Web Service Recommendation](https://mlsanity.com/paper/2401.03162/) (2024)
- [Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems](https://mlsanity.com/paper/2408.09646/) (2024)
- [AdaptGOT: A Pre-trained Model for Adaptive Contextual POI Representation Learning](https://mlsanity.com/paper/2506.21612/) (2025)

## Abstract (arXiv)

Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the collaborative signals from the high-order neighbors. Like other GCN models, the GCN based recommendation models also suffer from the notorious over-smoothing problem - when stacking more layers, node embeddings become more similar and eventually indistinguishable, resulted in performance degradation. The recently proposed LightGCN and LR-GCN alleviate this problem to some extent, however, we argue that they overlook an important factor for the over-smoothing problem in recommendation, that is, high-order neighboring users with no common interests of a user can be also involved in the user's embedding learning in the graph convolution operation. As a result, the multi-layer graph convolution will make users with dissimilar interests have similar embeddings. In this paper, we propose a novel Interest-aware Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order graph convolution inside subgraphs. The subgraph consists of users with similar interests and their interacted items. To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure. To this end, our model can avoid propagating negative information from high-order neighbors into embedding learning. Experimental results on three large-scale benchmark datasets show that our model can gain performance improvement by stacking more layers and outperform the state-of-the-art GCN-based recommendation models significantly.