# Style2Vec: Representation Learning for Fashion Items from Style Sets

arXiv: https://arxiv.org/abs/1708.04014 · HTML: https://mlsanity.com/paper/1708.04014/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2017.

## Built on by (most cited first, 8 of 8)

- [Design2Cloth: 3D Cloth Generation from 2D Masks](https://mlsanity.com/paper/2404.02686/) (2024)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendation](https://mlsanity.com/paper/2508.11105/) (2025)
- [Lost Your Style? Navigating with Semantic-Level Approach for Text-to-Outfit Retrieval](https://mlsanity.com/paper/2311.02122/) (2023)
- [Technologies for AI-Driven Fashion Social Networking Service with E-Commerce](https://mlsanity.com/paper/2203.10996/) (2022)
- [Recommendation of Compatible Outfits Conditioned on Style](https://mlsanity.com/paper/2203.16161/) (2022)
- [Outfit Generation and Recommendation -- An Experimental Study](https://mlsanity.com/paper/2211.16353/) (2022)
- [AI Tailoring: Evaluating Influence of Image Features on Fashion Product Popularity](https://mlsanity.com/paper/2411.14737/) (2024)

## Abstract (arXiv)

With the rapid growth of online fashion market, demand for effective fashion recommendation systems has never been greater. In fashion recommendation, the ability to find items that goes well with a few other items based on style is more important than picking a single item based on the user's entire purchase history. Since the same user may have purchased dress suits in one month and casual denims in another, it is impossible to learn the latent style features of those items using only the user ratings. If we were able to represent the style features of fashion items in a reasonable way, we will be able to recommend new items that conform to some small subset of pre-purchased items that make up a coherent style set. We propose Style2Vec, a vector representation model for fashion items. Based on the intuition of distributional semantics used in word embeddings, Style2Vec learns the representation of a fashion item using other items in matching outfits as context. Two different convolutional neural networks are trained to maximize the probability of item co-occurrences. For evaluation, a fashion analogy test is conducted to show that the resulting representation connotes diverse fashion related semantics like shapes, colors, patterns and even latent styles. We also perform style classification using Style2Vec features and show that our method outperforms other baselines.