# Style Similarity as Feedback for Product Design

arXiv: https://arxiv.org/abs/2105.12256 · HTML: https://mlsanity.com/paper/2105.12256/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Recognizing Image Style](https://mlsanity.com/paper/1311.3715/) (2013)
- [Visual Search at Pinterest](https://mlsanity.com/paper/1505.07647/) (2015)
- [Learning the Latent "Look": Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images](https://mlsanity.com/paper/1707.03376/) (2017)
- [An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures](https://mlsanity.com/paper/1505.03358/) (2015)
- [Fast and Scalable Position-Based Layout Synthesis](https://mlsanity.com/paper/1809.10526/) (2018)
- [Seeing Behind the Camera: Identifying the Authorship of a Photograph](https://mlsanity.com/paper/1508.05038/) (2015)

## Abstract (arXiv)

Matching and recommending products is beneficial for both customers and companies. With the rapid increase in home goods e-commerce, there is an increasing demand for quantitative methods for providing such recommendations for millions of products. This approach is facilitated largely by online stores such as Amazon and Wayfair, in which the goal is to maximize overall sales. Instead of focusing on overall sales, we take a product design perspective, by employing big-data analysis for determining the design qualities of a highly recommended product. Specifically, we focus on the visual style compatibility of such products. We build off previous work which implemented a style-based similarity metric for thousands of furniture products. Using analysis and visualization, we extract attributes of furniture products that are highly compatible style-wise. We propose a designer in-the-loop workflow that mirrors methods of displaying similar products to consumers browsing e-commerce websites. Our findings are useful when designing new products, since they provide insight regarding what furniture will be strongly compatible across multiple styles, and hence, more likely to be recommended.