# IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation

arXiv: https://arxiv.org/abs/1907.12377 · HTML: https://mlsanity.com/paper/1907.12377/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 19 of 19)

- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning](https://mlsanity.com/paper/2105.07944/) (2021)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2203.16218/) (2022)
- [RARE: Robust Masked Graph Autoencoder](https://mlsanity.com/paper/2304.01507/) (2023)
- [Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation](https://mlsanity.com/paper/2203.16097/) (2022)
- [GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy](https://mlsanity.com/paper/2312.09708/) (2023)
- [Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2202.09177/) (2022)
- [Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations](https://mlsanity.com/paper/2107.05235/) (2021)
- [GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing](https://mlsanity.com/paper/2111.00680/) (2021)
- [SHGNN: Structure-Aware Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2112.06244/) (2021)
- [NudgeRank: Digital Algorithmic Nudging for Personalized Health](https://mlsanity.com/paper/2407.20241/) (2024)
- [G3SR: Global Graph Guided Session-based Recommendation](https://mlsanity.com/paper/2203.06467/) (2022)
- [Flattened Graph Convolutional Networks For Recommendation](https://mlsanity.com/paper/2210.07769/) (2022)
- [Review of Explainable Graph-Based Recommender Systems](https://mlsanity.com/paper/2408.00166/) (2024)
- [Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation](https://mlsanity.com/paper/2206.13255/) (2022)
- [GPatch: Patching Graph Neural Networks for Cold-Start Recommendations](https://mlsanity.com/paper/2209.12215/) (2022)
- [Multi-Granular Attention based Heterogeneous Hypergraph Neural Network](https://mlsanity.com/paper/2505.04340/) (2025)

## Abstract (arXiv)

The remarkable progress of network embedding has led to state-of-the-art algorithms in recommendation. However, the sparsity of user-item interactions (i.e., explicit preferences) on websites remains a big challenge for predicting users' behaviors. Although research efforts have been made in utilizing some auxiliary information (e.g., social relations between users) to solve the problem, the existing rich heterogeneous auxiliary relationships are still not fully exploited. Moreover, previous works relied on linearly combined regularizers and suffered parameter tuning. In this work, we collect abundant relationships from common user behaviors and item information, and propose a novel framework named IntentGC to leverage both explicit preferences and heterogeneous relationships by graph convolutional networks. In addition to the capability of modeling heterogeneity, IntentGC can learn the importance of different relationships automatically by the neural model in a nonlinear sense. To apply IntentGC to web-scale applications, we design a faster graph convolutional model named IntentNet by avoiding unnecessary feature interactions. Empirical experiments on two large-scale real-world datasets and online A/B tests in Alibaba demonstrate the superiority of our method over state-of-the-art algorithms.