# Fashion Recommendation Based on Style and Social Events

arXiv: https://arxiv.org/abs/2208.00725 · HTML: https://mlsanity.com/paper/2208.00725/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 1 Aug 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [Learning Fashion Compatibility with Bidirectional LSTMs](https://mlsanity.com/paper/1707.05691/) (2017)
- [MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction](https://mlsanity.com/paper/2006.03340/) (2020)
- [Learning Type-Aware Embeddings for Fashion Compatibility](https://mlsanity.com/paper/1803.09196/) (2018)
- [Context-Aware Visual Compatibility Prediction](https://mlsanity.com/paper/1902.03646/) (2019)
- [Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks](https://mlsanity.com/paper/1902.08009/) (2019)
- [SMEMO: Social Memory for Trajectory Forecasting](https://mlsanity.com/paper/2203.12446/) (2022)
- [Neural Compatibility Modeling with Attentive Knowledge Distillation](https://mlsanity.com/paper/1805.00313/) (2018)
- [Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network](https://mlsanity.com/paper/1907.11496/) (2019)
- [Garment Recommendation with Memory Augmented Neural Networks](https://mlsanity.com/paper/2012.06200/) (2020)
- [PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability](https://mlsanity.com/paper/2008.01780/) (2020)
- [Segmentation Free Object Discovery in Video](https://mlsanity.com/paper/1609.00221/) (2016)
- [Interpretable Partitioned Embedding for Customized Fashion Outfit Composition](https://mlsanity.com/paper/1806.04845/) (2018)

## Built on by (most cited first, 3 of 3)

- [Transformer-based Graph Neural Networks for Outfit Generation](https://mlsanity.com/paper/2304.08098/) (2023)
- [Interactive Garment Recommendation with User in the Loop](https://mlsanity.com/paper/2402.11627/) (2024)
- [Garment Attribute Manipulation with Multi-level Attention](https://mlsanity.com/paper/2409.10206/) (2024)

## Abstract (arXiv)

Fashion recommendation is often declined as the task of finding complementary items given a query garment or retrieving outfits that are suitable for a given user. In this work we address the problem by adding an additional semantic layer based on the style of the proposed dressing. We model style according to two important aspects: the mood and the emotion concealed behind color combination patterns and the appropriateness of the retrieved garments for a given type of social event. To address the former we rely on Shigenobu Kobayashi's color image scale, which associated emotional patterns and moods to color triples. The latter instead is analyzed by extracting garments from images of social events. Overall, we integrate in a state of the art garment recommendation framework a style classifier and an event classifier in order to condition recommendation on a given query.