# Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

arXiv: https://arxiv.org/abs/2005.12566 · HTML: https://mlsanity.com/paper/2005.12566/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 27 of 27)

- [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Extending CLIP for Category-to-image Retrieval in E-commerce](https://mlsanity.com/paper/2112.11294/) (2021)
- [Counterfactual Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.01391/) (2023)
- [Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation](https://mlsanity.com/paper/2111.00678/) (2021)
- [Multimodality Invariant Learning for Multimedia-Based New Item Recommendation](https://mlsanity.com/paper/2405.15783/) (2024)
- [Fine-tuning Multimodal Large Language Models for Product Bundling](https://mlsanity.com/paper/2407.11712/) (2024)
- [Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation](https://mlsanity.com/paper/2306.14179/) (2023)
- [Exploring AI in Fashion: A Review of Aesthetics, Personalization, Virtual Try-On, and Forecasting](https://mlsanity.com/paper/2101.08301/) (2021)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Learning to Synthesize Compatible Fashion Items Using Semantic Alignment and Collocation Classification: An Outfit Generation Framework](https://mlsanity.com/paper/2502.06827/) (2025)
- [Transformer-based Graph Neural Networks for Outfit Generation](https://mlsanity.com/paper/2304.08098/) (2023)
- [Poincar'e Heterogeneous Graph Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/2205.11233/) (2022)
- [Decoding Style: Efficient Fine-Tuning of LLMs for Image-Guided Outfit Recommendation with Preference](https://mlsanity.com/paper/2409.12150/) (2024)
- [COutfitGAN: Learning to Synthesize Compatible Outfits Supervised by Silhouette Masks and Fashion Styles](https://mlsanity.com/paper/2502.08674/) (2025)
- [FashionM3: Multimodal, Multitask, and Multiround Fashion Assistant based on Unified Vision-Language Model](https://mlsanity.com/paper/2504.17826/) (2025)
- [Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendation](https://mlsanity.com/paper/2508.11105/) (2025)
- [RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation](https://mlsanity.com/paper/2509.03131/) (2025)
- [TATTOO: Training-free AesTheTic-aware Outfit recOmmendation](https://mlsanity.com/paper/2509.23242/) (2025)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [Personalized Fashion Recommendation with Image Attributes and Aesthetics Assessment](https://mlsanity.com/paper/2501.03085/) (2025)
- [Recommendation of Compatible Outfits Conditioned on Style](https://mlsanity.com/paper/2203.16161/) (2022)
- [Dress Well via Fashion Cognitive Learning](https://mlsanity.com/paper/2208.00639/) (2022)
- [ICAR: Image-based Complementary Auto Reasoning](https://mlsanity.com/paper/2308.09119/) (2023)
- [BC-GAN: A Generative Adversarial Network for Synthesizing a Batch of Collocated Clothing](https://mlsanity.com/paper/2502.01080/) (2025)
- [Recommending Composite Items Using Multi-Level Preference Information: A Joint Interaction Modeling Approach](https://mlsanity.com/paper/2601.19005/) (2026)
- [Autonomous Fashion Outfit Composition via Unified Aesthetic Foresight Model](https://mlsanity.com/paper/2608.13888/) (2026)

## Abstract (arXiv)

Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities.Distinct from other scenarios (e.g., social networking or content sharing) which recommend a single item (e.g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items.Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference. However, present works focus mainly on one of the requirements and only consider either user-outfit or outfit-item relationships, thereby easily leading to suboptimal representations and limiting the performance. In this work, we unify two tasks, fashion compatibility modeling and personalized outfit recommendation. Towards this end, we develop a new framework, Hierarchical Fashion Graph Network(HFGN), to model relationships among users, items, and outfits simultaneously. In particular, we construct a hierarchical structure upon user-outfit interactions and outfit-item mappings. We then get inspirations from recent graph neural networks, and employ the embedding propagation on such hierarchical graph, so as to aggregate item information into an outfit representation, and then refine a user's representation via his/her historical outfits. Furthermore, we jointly train these two tasks to optimize these representations. To demonstrate the effectiveness of HFGN, we conduct extensive experiments on a benchmark dataset, and HFGN achieves significant improvements over the state-of-the-art compatibility matching models like NGNN and outfit recommenders like FHN.