# Category Prediction for Search Query Understanding

DevFeed: [Category Prediction for Search Query Understanding](<https://devfeed.tech/articles/category-prediction-for-search-query-understanding-20133.md>)

Original publisher: [Read original article](<https://medium.com/myntra-engineering/category-prediction-for-search-query-understanding-f46283151c92?source=rss----7484818e9f88---4>)

Author: music and waves

Published: 2024-04-21T12:40:48Z

Content type: article

Language: en

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

Topics: [Query (disambiguation)](<https://devfeed.tech/topics/query.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [classification](<https://devfeed.tech/tags/classification.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [ecommerce](<https://devfeed.tech/tags/ecommerce.md>), [information-retrieval](<https://devfeed.tech/tags/information-retrieval.md>), [model](<https://devfeed.tech/tags/model.md>), [neural](<https://devfeed.tech/tags/neural.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [search](<https://devfeed.tech/tags/search.md>), [text-classification](<https://devfeed.tech/tags/text-classification.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

This article describes Myntra's multi-label product-category classification model for understanding ambiguous search queries. It covers preparing query-and-category training data and training a neural text classifier to predict relevant categories for live searches.

## Source excerpt

Navigating through online shopping platforms can sometimes feel like finding your way through a maze. Take the search bar, for example. You type in "winter upper wear," hoping to find the perfect jacket or cozy sweatshirt. But here's the tricky part: the search engine has to decipher what you mean. Is it jackets you're after? Or maybe sweatshirts? Or both? It gets even more confusing when you consider the overlapping categories. Kurtas can be standalone articles or part of kurta sets. And loafers? They could belong to formal shoes or casual shoes and certainly not sports shoes. See the challenge? To tackle this, Myntra uses a multi-label search to product category classification model. It's like having an assistant that can understand possible intents from your search query. So when you type in something like "whey," the model knows you might be looking for protein or health supplements. But here's the catch: search queries can be short and vague, and they often use words that don't directly match category names. People might search using different terms or even regional variations. So, the model needs to be clever enough to map those words to the right categories internally. The goal is to capture all possible intents without cluttering your search results with irrelevant stuff. After all, nobody likes sifting through pages of irrelevant products. It's a delicate balance between covering all bases and keeping things tidy. Solution The solution has 2 major components. I. Data preparation We prepare ( search query : categories ) data points to be consumed in training by the neural classifier. Ex. ( ethnic wear : kurta, sarees ) II. Training a neural model We train a neural multi-label text classifier that consumes the prepared training data which is used to predicts categories for search queries live. I. Data Preparation We generate the supervised text classification training data in form of a search query and its product category(s) as its labels. This set is enrich