# Relation-aware Heterogeneous Graph for User Profiling

arXiv: https://arxiv.org/abs/2110.07181 · HTML: https://mlsanity.com/paper/2110.07181/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [DeepInf: Social Influence Prediction with Deep Learning](https://mlsanity.com/paper/1807.05560/) (2018)
- [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021)
- [Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks](https://mlsanity.com/paper/2004.13826/) (2020)
- [TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding](https://mlsanity.com/paper/2006.01321/) (2020)
- [Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation](https://mlsanity.com/paper/2103.03578/) (2021)
- [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2005.02844/) (2020)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [Semi-supervised User Geolocation via Graph Convolutional Networks](https://mlsanity.com/paper/1804.08049/) (2018)
- [Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval](https://mlsanity.com/paper/2102.11127/) (2021)
- [A Graph-based Relevance Matching Model for Ad-hoc Retrieval](https://mlsanity.com/paper/2101.11873/) (2021)

## Built on by (most cited first, 9 of 9)

- [When Do LLMs Help With Node Classification? A Comprehensive Analysis](https://mlsanity.com/paper/2502.00829/) (2025)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables](https://mlsanity.com/paper/2402.13028/) (2024)
- [MAPN: Enhancing Heterogeneous Sparse Graph Representation by Mamba-based Asynchronous Aggregation](https://mlsanity.com/paper/2502.16454/) (2025)
- [Enriching Semantic Profiles into Knowledge Graph for Recommender Systems Using Large Language Models](https://mlsanity.com/paper/2601.08148/) (2026)
- [CPT: Competence-progressive Training Strategy for Few-shot Node Classification](https://mlsanity.com/paper/2402.00450/) (2024)
- [FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths](https://mlsanity.com/paper/2502.16281/) (2025)
- [REMEDI: Relative Feature Enhanced Meta-Learning with Distillation for Imbalanced Prediction](https://mlsanity.com/paper/2505.07245/) (2025)
- [Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/2607.19253/) (2026)

## Abstract (arXiv)

User profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entities of a graph and turn the problem into a node classification task. However, they neglect the difference of distinct interaction types, e.g. user clicks an item v.s.user purchases an item, and thus cannot incorporate such information well. To solve these issues, we propose to leverage the relation-aware heterogeneous graph method for user profiling, which also allows capturing significant meta relations. We adopt the query, key, and value mechanism in a transformer fashion for heterogeneous message passing so that entities can effectively interact with each other. Via such interactions on different relation types, our model can generate representations with rich information for the user profile prediction. We conduct experiments on two real-world e-commerce datasets and observe a significant performance boost of our approach.