# fit-and-size

Published articles for fit-and-size.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Size Recommendation System at Myntra

DevFeed: [Size Recommendation System at Myntra](<https://devfeed.tech/articles/size-recommendation-system-at-myntra-20140.md>)

Original publisher: [Read original article](<https://medium.com/myntra-engineering/size-recommendation-system-at-myntra-58cb4870caa5?source=rss----7484818e9f88---4>)

Author: Aayushi Das

Published: 2025-01-30T06:25:43Z

Content type: article

Language: en

Sources: [Myntra](<https://devfeed.tech/sources/myntra.md>)

Topics: [Data Science](<https://devfeed.tech/topics/data-science.md>), [implementation](<https://devfeed.tech/topics/implementation.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [Database](<https://devfeed.tech/topics/database.md>)

Tags: [blog](<https://devfeed.tech/tags/blog.md>), [dashboards](<https://devfeed.tech/tags/dashboards.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [e-commerce](<https://devfeed.tech/tags/e-commerce.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [fit-and-size](<https://devfeed.tech/tags/fit-and-size.md>), [implementation](<https://devfeed.tech/tags/implementation.md>), [models](<https://devfeed.tech/tags/models.md>), [online-shopping](<https://devfeed.tech/tags/online-shopping.md>), [performance](<https://devfeed.tech/tags/performance.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [retail](<https://devfeed.tech/tags/retail.md>)

### AI overview

This article describes Myntra's Size Recommendation System for personalized clothing size and fit recommendations. It explains how the system uses users' past purchases and Try Size Finder inputs, provides real-time recommendations, monitors performance with dashboards, and refreshes models and vectors through offline pipelines.

### Source excerpt

In recent years, online shopping has surged, revolutionizing how people purchase products and services. E-commerce's convenience has reshaped consumer behaviour and the retail landscape. Unlike traditional stores, online shoppers often face sizing challenges, leading to hesitancy and missed sales. Myntra has been a pioneer in addressing size and fit challenges in India, leading the way with innovative solutions that have significantly enhanced the shopping experience. Building on its leadership in this space, Myntra's latest initiatives take these solutions to the next level, offering even sharper and more effective recommendations. Solving this complex problem requires a combination of various features addressing size and fit issues. This blog details Myntra's approach to size and fit recommendations, including our solution, implementation, offline pipelines, online services, handling size recommendation leakages, A/B analysis and more, providing a comprehensive overview of our strategies and outcomes. What is the solution being used at Myntra? Personalized recommendations are generated using data science models, which rely on two main types of inputs - Past purchases of the user Size and fit inputs provided through the "Try Size Finder" questionnaire for users without purchase history Image 1: Recommendation based on past purchasesImage 2: Recommendation based on user inputsHow have we implemented this solution? We have implemented a Size Recommendation System (SRS) to personalize size and fit recommendations for Myntra users, enhancing their shopping experience. This end-to-end service provides real-time size recommendations based on user profiles, purchase history and inputs, ensuring a seamless and engaging buying process. System performance is monitored via dashboards with appropriate alerts. Offline pipelines The models tend to degrade over time due to drifts. To address this issue, we have established refresh cycles at suitable frequencies to maintain high c