# Multi-Component Graph Convolutional Collaborative Filtering

arXiv: https://arxiv.org/abs/1911.10699 · HTML: https://mlsanity.com/paper/1911.10699/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 17 of 17)

- [Contrastive Meta Learning with Behavior Multiplicity for Recommendation](https://mlsanity.com/paper/2202.08523/) (2022)
- [Disentangled Contrastive Collaborative Filtering](https://mlsanity.com/paper/2305.02759/) (2023)
- [CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs](https://mlsanity.com/paper/2202.07826/) (2022)
- [SiReN: Sign-Aware Recommendation Using Graph Neural Networks](https://mlsanity.com/paper/2108.08735/) (2021)
- [The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm](https://mlsanity.com/paper/2209.15292/) (2022)
- [Improved Diversity-Promoting Collaborative Metric Learning for Recommendation](https://mlsanity.com/paper/2409.01012/) (2024)
- [Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation](https://mlsanity.com/paper/2106.03415/) (2021)
- [ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning](https://mlsanity.com/paper/2501.10640/) (2025)
- [Adversarial Graph Disentanglement](https://mlsanity.com/paper/2103.07295/) (2021)
- [ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest](https://mlsanity.com/paper/2203.12596/) (2022)
- [AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching](https://mlsanity.com/paper/2208.08606/) (2022)
- [Flexible Generation of Preference Data for Recommendation Analysis](https://mlsanity.com/paper/2407.16594/) (2024)
- [Attention-Based Recommendation On Graphs](https://mlsanity.com/paper/2201.05499/) (2022)
- [A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data](https://mlsanity.com/paper/2204.07819/) (2022)
- [KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation](https://mlsanity.com/paper/2205.12102/) (2022)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [MixRec: Heterogeneous Graph Collaborative Filtering](https://mlsanity.com/paper/2412.13825/) (2024)

## Abstract (arXiv)

The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort in exploring user-item graph for collaborative filtering methods. Nevertheless, the formation of user-item interactions typically arises from highly complex latent purchasing motivations, such as high cost performance or eye-catching appearance, which are indistinguishably represented by the edges. The existing approaches still remain the differences between various purchasing motivations unexplored, rendering the inability to capture fine-grained user preference. Therefore, in this paper we propose a novel Multi-Component graph convolutional Collaborative Filtering (MCCF) approach to distinguish the latent purchasing motivations underneath the observed explicit user-item interactions. Specifically, there are two elaborately designed modules, decomposer and combiner, inside MCCF. The former first decomposes the edges in user-item graph to identify the latent components that may cause the purchasing relationship; the latter then recombines these latent components automatically to obtain unified embeddings for prediction. Furthermore, the sparse regularizer and weighted random sample strategy are utilized to alleviate the overfitting problem and accelerate the optimization. Empirical results on three real datasets and a synthetic dataset not only show the significant performance gains of MCCF, but also well demonstrate the necessity of considering multiple components.