搜索查询理解的类别预测

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?

在在线购物平台上导航有时感觉像在迷宫中寻路。以搜索栏为例，你输入“冬季上衣”，希望找到完美的夹克或舒适的运动衫。但这里有个棘手的问题：搜索引擎必须解读你的意思。你是想要夹克吗？还是运动衫？还是两者都要？

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?

当考虑到重叠的类别时，情况变得更加混乱。Kurtas可以是独立的物品，也可以是Kurta套装的一部分。而loafers呢？它们可能属于正式鞋或休闲鞋，肯定不是运动鞋。看到挑战了吗？

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.

为了解决这个问题，Myntra使用了一个多标签搜索到产品类别分类模型。就像有一个能够理解搜索查询中可能意图的助手一样。所以当你输入类似于“乳清”的东西时，模型知道你可能在寻找蛋白质或健康补充剂。

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.

但是这里有个问题：搜索查询可能很短，含糊不清，它们经常使用与类别名称不直接匹配的词语。人们可能使用不同的术语甚至是地区变体进行搜索。因此，模型需要足够聪明，能够将这些词语内部地映射到正确的类别。

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.

目标是捕捉所有可能的意图，而不会在搜索结果中混杂无关的内容。毕竟，没有人喜欢在无关产品的页面中筛选。这是在涵盖所有基础的同时保持整洁的微妙平衡。

Solution

解决方案

The solution has 2 major components.

该解决方案有两个主要组成部分。

**I. Data preparation**We prepare ( search query : categories ) data points ...