# Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach

arXiv: https://arxiv.org/abs/2005.12439 · HTML: https://mlsanity.com/paper/2005.12439/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 5 of 5)

- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense](https://mlsanity.com/paper/2304.05135/) (2023)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [Dress Well via Fashion Cognitive Learning](https://mlsanity.com/paper/2208.00639/) (2022)
- [Fashion Object Detection for Tops & Bottoms](https://mlsanity.com/paper/2305.18482/) (2023)

## Abstract (arXiv)

With the growth of online shopping for fashion products, accurate fashion recommendation has become a critical problem. Meanwhile, social networks provide an open and new data source for personalized fashion analysis. In this work, we study the problem of personalized fashion recommendation from social media data, i.e. recommending new outfits to social media users that fit their fashion preferences. To this end, we present an item-to-set metric learning framework that learns to compute the similarity between a set of historical fashion items of a user to a new fashion item. To extract features from multi-modal street-view fashion items, we propose an embedding module that performs multi-modality feature extraction and cross-modality gated fusion. To validate the effectiveness of our approach, we collect a real-world social media dataset. Extensive experiments on the collected dataset show the superior performance of our proposed approach.