# Multi-Graph Convolution Collaborative Filtering

arXiv: https://arxiv.org/abs/2001.00267 · HTML: https://mlsanity.com/paper/2001.00267/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2020.

## Built on by (most cited first, 27 of 27)

- [Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning](https://mlsanity.com/paper/2202.06200/) (2022)
- [Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network](https://mlsanity.com/paper/2110.04002/) (2021)
- [GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations](https://mlsanity.com/paper/2208.04222/) (2022)
- [MMREC: LLM Based Multi-Modal Recommender System](https://mlsanity.com/paper/2408.04211/) (2024)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Knowledge-Enhanced Top-K Recommendation in Poincar'e Ball](https://mlsanity.com/paper/2101.04852/) (2021)
- [Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation](https://mlsanity.com/paper/2109.02046/) (2021)
- [Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation](https://mlsanity.com/paper/2101.04849/) (2021)
- [Model Inversion Attacks: A Survey of Approaches and Countermeasures](https://mlsanity.com/paper/2411.10023/) (2024)
- [DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systems](https://mlsanity.com/paper/2512.00596/) (2025)
- [Node Copying: A Random Graph Model for Effective Graph Sampling](https://mlsanity.com/paper/2208.02435/) (2022)
- [Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation](https://mlsanity.com/paper/2208.01709/) (2022)
- [Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding](https://mlsanity.com/paper/2312.16834/) (2023)
- [Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations](https://mlsanity.com/paper/2503.18001/) (2025)
- [Graph Convolutional Embeddings for Recommender Systems](https://mlsanity.com/paper/2103.03587/) (2021)
- [Attention-Based Recommendation On Graphs](https://mlsanity.com/paper/2201.05499/) (2022)
- [Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering](https://mlsanity.com/paper/2203.07463/) (2022)
- [Personalized Negative Reservoir for Incremental Learning in Recommender Systems](https://mlsanity.com/paper/2403.03993/) (2024)
- [Interpretable Triplet Importance for Personalized Ranking](https://mlsanity.com/paper/2407.19469/) (2024)
- [Mini-Game Lifetime Value Prediction in WeChat](https://mlsanity.com/paper/2506.11037/) (2025)
- [Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation](https://mlsanity.com/paper/2211.06370/) (2022)
- [Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems](https://mlsanity.com/paper/2312.11486/) (2023)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [Dual Adversarial Perturbators Generate rich Views for Recommendation](https://mlsanity.com/paper/2409.06719/) (2024)
- [Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation](https://mlsanity.com/paper/2502.08685/) (2025)
- [Joint Behavior-guided and Modality-coherence Conditional Graph Diffusion Denoising for Multi Modal Recommendation](https://mlsanity.com/paper/2604.03654/) (2026)
- [Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification](https://mlsanity.com/paper/2605.12699/) (2026)

## Abstract (arXiv)

Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users and items with the goal of predicting a user's preference for an item based on the similarity of the representations. Techniques range from classic matrix factorization to more recent deep learning based methods. However, we argue that existing methods do not make full use of the information that is available from user-item interaction data and the similarities between user pairs and item pairs. In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process. Multi-GCCF not only expressively models the high-order information via a partite user-item interaction graph, but also integrates the proximal information by building and processing user-user and item-item graphs. Furthermore, we consider the intrinsic difference between user nodes and item nodes when performing graph convolution on the bipartite graph. We conduct extensive experiments on four publicly accessible benchmarks, showing significant improvements relative to several state-of-the-art collaborative filtering and graph neural network-based recommendation models. Further experiments quantitatively verify the effectiveness of each component of our proposed model and demonstrate that the learned embeddings capture the important relationship structure.