Personalizing Similar Product Recommendations in Fashion E-commerce
This addresses the user experience issue for fashion e-commerce shoppers by personalizing recommendations, though it appears incremental as it marries existing approaches.
The paper tackles the problem of non-personalized similar product recommendations in fashion e-commerce by combining collaborative filtering with personalization, resulting in improved key metrics on the Myntra platform.
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.