# Location-Based Fashion Recommendations at Myntra

DevFeed: [Location-Based Fashion Recommendations at Myntra](<https://devfeed.tech/articles/decoding-the-regional-fashion-signatures-using-ai-20134.md>)

Original publisher: [Read original article](<https://medium.com/myntra-engineering/decoding-the-fashion-signature-using-embeddings-b21221806b7d?source=rss----7484818e9f88---4>)

Author: Siddhartha Devapujula

Published: 2024-04-22T11:25:37Z

Content type: tutorial

Language: en

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

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [recommendations](<https://devfeed.tech/topics/recommendations.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [ecommerce](<https://devfeed.tech/tags/ecommerce.md>), [fashion](<https://devfeed.tech/tags/fashion.md>), [location-based](<https://devfeed.tech/tags/location-based.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [personalisation](<https://devfeed.tech/tags/personalisation.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [search](<https://devfeed.tech/tags/search.md>)

## AI overview

Myntra explains how machine-learning recommendation systems can use location as user side information to improve personalized fashion recommendations, including for cold-start users.

## Source excerpt

Authored By Rohit Gupta & Siddhartha Devapujula Introduction Millions of users visit Myntra daily to upgrade their wardrobes and millions of items are listed on the platform at any given time. Users neither have the time nor the capability to scroll through this vast list of items. Even after applying category and attribute filters, usually the number of items is still in thousands. Hence it becomes critical that the top search results for any user are both relevant and personalized. Just like search, many other recommendation widgets across the platform face the same challenges. Fashion Diversity -- Every Region has its own Fashion Showing each user the best styles for them from a catalog of million plus products is where machine learning based recommendation systems come into play. From search results on google to your netflix home screen, recommendation systems are working in the background to get you the best results. It is impossible to imagine modern age internet experience without these systems. The uber goal of these models is to take the user features and the vast list of items as input ,and generate a small personalized list of items for each user. For these systems to work, we mainly use the user's historical activity on the platform. In this blog we will see how using other kinds of user details can also enhance the quality of recommendations. In the next sections, we dive into the details of recommendation systems and related techniques. We explain the motivation for a location based recommendation system and how we built one at Myntra. Later we discuss a few use cases at Myntra, results and potential future work. Basics of recommendation systems This is a very simple read about recommendation systems by Google -- Recommendations: What and Why? | Machine Learning | Google for Developers. Readers can skip if they are already aware of this. Traditional recommendation models focus on using the user's historical interactions on the platform to learn. This wor