使用嵌入解码时尚签名

Authored By Rohit Gupta & Siddhartha Devapujula

Introduction

介绍

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.

每天有数百万用户访问Myntra来升级他们的衣柜，平台上同时列出了数百万件商品。用户既没有时间也没有能力浏览这个庞大的商品列表。即使应用了类别和属性过滤器，通常物品的数量仍然在数千个。因此，对于任何用户来说，最重要的是搜索结果既相关又个性化。就像搜索一样，平台上的许多其他推荐小部件也面临着相同的挑战。

Fashion Diversity — Every Region has its own Fashion

时尚多样性 - 每个地区都有自己的时尚

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.

从百万级产品目录中为每个用户展示最适合他们的款式是基于机器学习的推荐系统的应用场景。从谷歌的搜索结果到Netflix的主页，推荐系统在背后工作，为您提供最佳结果。现代互联网体验中无法想象没有这些系统。

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.

这些模型的终极目标是将用户特征和庞大的项目列表作为输入，为每个用户生成一个小的个性化项目列表。

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.

为了使这些系统正常工作，我们主要使用用户在平台上的历史活动。在本博客中，我们将看到如何使用其他类型的用户详细信息来提高推荐的质量。

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, res...