# A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation

arXiv: https://arxiv.org/abs/2407.19886 · HTML: https://mlsanity.com/paper/2407.19886/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts](https://mlsanity.com/paper/2111.02358/) (2021)
- [Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey](https://mlsanity.com/paper/2302.10035/) (2023)
- [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://mlsanity.com/paper/1510.01784/) (2015)
- [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021)
- [Bootstrap Latent Representations for Multi-modal Recommendation](https://mlsanity.com/paper/2207.05969/) (2022)
- [A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation](https://mlsanity.com/paper/2211.06924/) (2022)
- [Contrastive Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2304.03891/) (2023)
- [Graph Transformer for Recommendation](https://mlsanity.com/paper/2306.02330/) (2023)
- [Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval](https://mlsanity.com/paper/2108.02417/) (2021)
- [SIGformer: Sign-aware Graph Transformer for Recommendation](https://mlsanity.com/paper/2404.11982/) (2024)
- [A Directional Diffusion Graph Transformer for Recommendation](https://mlsanity.com/paper/2404.03326/) (2024)
- [3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting](https://mlsanity.com/paper/2404.17273/) (2024)
- [Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning](https://mlsanity.com/paper/2207.13909/) (2022)
- [Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation](https://mlsanity.com/paper/2111.10539/) (2021)

## Built on by (most cited first, 4 of 4)

- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment](https://mlsanity.com/paper/2602.00682/) (2026)
- [KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation](https://mlsanity.com/paper/2507.05863/) (2025)
- [You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control](https://mlsanity.com/paper/2507.16829/) (2025)

## Abstract (arXiv)

With the rapid development of online multimedia services, especially in e-commerce platforms, there is a pressing need for personalised recommendation systems that can effectively encode the diverse multi-modal content associated with each item. However, we argue that existing multi-modal recommender systems typically use isolated processes for both feature extraction and modality modelling. Such isolated processes can harm the recommendation performance. Firstly, an isolated extraction process underestimates the importance of effective feature extraction in multi-modal recommendations, potentially incorporating non-relevant information, which is harmful to item representations. Second, an isolated modality modelling process produces disjointed embeddings for item modalities due to the individual processing of each modality, which leads to a suboptimal fusion of user/item representations for effective user preferences prediction. We hypothesise that the use of a unified model for addressing both aforementioned isolated processes will enable the consistent extraction and cohesive fusion of joint multi-modal features, thereby enhancing the effectiveness of multi-modal recommender systems. In this paper, we propose a novel model, called Unified Multi-modal Graph Transformer (UGT), which firstly leverages a multi-way transformer to extract aligned multi-modal features from raw data for top-k recommendation. Subsequently, we build a unified graph neural network in our UGT model to jointly fuse the user/item representations with their corresponding multi-modal features. Using the graph transformer architecture of our UGT model, we show that the UGT model can achieve significant effectiveness gains, especially when jointly optimised with the commonly-used multi-modal recommendation losses.