# Personalizing Similar Product Recommendations in Fashion E-commerce

arXiv: https://arxiv.org/abs/1806.11371 · HTML: https://mlsanity.com/paper/1806.11371/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2018.

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

- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Enhancing E-Commerce Recommendation using Pre-Trained Language Model and Fine-Tuning](https://mlsanity.com/paper/2302.04443/) (2023)
- [Human Body Shape Classification Based on a Single Image](https://mlsanity.com/paper/2305.18480/) (2023)

## Abstract (arXiv)

In fashion e-commerce platforms, product discovery is one of the key components of a good user experience. There are numerous ways using which people find the products they desire. Similar product recommendations is one of the popular modes using which users find products that resonate with their intent. Generally these recommendations are not personalized to a specific user. Traditionally, collaborative filtering based approaches have been popular in the literature for recommending non-personalized products given a query product. Also, there has been focus on personalizing the product listing for a given user. In this paper, we marry these approaches so that users will be recommended with personalized similar products. Our experimental results on a large fashion e-commerce platform (Myntra) show that we can improve the key metrics by applying personalization on similar product recommendations.