# Graph Convolution Machine for Context-aware Recommender System

arXiv: https://arxiv.org/abs/2001.11402 · HTML: https://mlsanity.com/paper/2001.11402/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 13 of 13)

- [Unified Parameter-Efficient Unlearning for LLMs](https://mlsanity.com/paper/2412.00383/) (2024)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation](https://mlsanity.com/paper/2411.00163/) (2024)
- [Graph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation](https://mlsanity.com/paper/2308.07592/) (2023)
- [Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding](https://mlsanity.com/paper/2407.16959/) (2024)
- [Deep Partial Multiplex Network Embedding](https://mlsanity.com/paper/2203.02656/) (2022)
- [ConvFormer: Revisiting Transformer for Sequential User Modeling](https://mlsanity.com/paper/2308.02925/) (2023)
- [Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization](https://mlsanity.com/paper/2408.01697/) (2024)
- [Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation](https://mlsanity.com/paper/2207.14218/) (2022)
- [Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender](https://mlsanity.com/paper/2306.00009/) (2023)
- [Context-aware explainable recommendations over knowledge graphs](https://mlsanity.com/paper/2310.16141/) (2023)
- [Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios](https://mlsanity.com/paper/2503.03524/) (2025)
- [Correct and Weight: A Simple Yet Effective Loss for Implicit Feedback Recommendation](https://mlsanity.com/paper/2601.04291/) (2026)

## Abstract (arXiv)

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to the collaborative filtering (CF) scenario, where the interaction contexts are not available. In this work, we extend the advantages of graph convolutions to context-aware recommender system (CARS, which represents a generic type of models that can handle various side information). We propose textitGraph Convolution Machine (GCM), an end-to-end framework that consists of three components: an encoder, graph convolution (GC) layers, and a decoder. The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph. The decoder digests the refined embeddings to output the prediction score by considering the interactions among user, item, and context embeddings. We conduct experiments on three real-world datasets from Yelp and Amazon, validating the effectiveness of GCM and the benefits of performing graph convolutions for CARS. Our implementations are available at urlhttps://github.com/wujcan/GCM.