# Deep Style Match for Complementary Recommendation

arXiv: https://arxiv.org/abs/1708.07938 · HTML: https://mlsanity.com/paper/1708.07938/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2017.

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

- [Semi-supervised Adversarial Learning for Complementary Item Recommendation](https://mlsanity.com/paper/2303.05812/) (2023)
- [Complementary Recommendation in E-commerce: Definition, Approaches, and Future Directions](https://mlsanity.com/paper/2403.16135/) (2024)
- [Recommending Composite Items Using Multi-Level Preference Information: A Joint Interaction Modeling Approach](https://mlsanity.com/paper/2601.19005/) (2026)

## Abstract (arXiv)

Humans develop a common sense of style compatibility between items based on their attributes. We seek to automatically answer questions like "Does this shirt go well with that pair of jeans?" In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper. The basic assumption of our approach is that most of the important attributes for a product in an online store are included in its title description. Therefore it is feasible to learn style compatibility from these descriptions. We design a Siamese Convolutional Neural Network architecture and feed it with title pairs of items, which are either compatible or incompatible. Those pairs will be mapped from the original space of symbolic words into some embedded style space. Our approach takes only words as the input with few preprocessing and there is no laborious and expensive feature engineering.