Putting together an ideal outfit is a process that involves creativity and
style intuition. This makes it a particularly difficult task to automate.
Existing styling products generally involve human specialists and a highly
curated set of fashion items. In this paper, we will describe how we
bootstrapped the Complete The Look (CTL) system at Pinterest. This is a
technology that aims to learn the subjective task of "style compatibility" in
order to recommend complementary items that complete an outfit. In particular,
we want to show recommendations from other categories that are compatible with
an item of interest. For example, what are some heels that go well with this
cocktail dress? We will introduce our outfit dataset of over 1 million outfits
and 4 million objects, a subset of which we will make available to the research
community, and describe the pipeline used to obtain and refresh this dataset.
Furthermore, we will describe how we evaluate this subjective task and compare
model performance across multiple training methods. Lastly, we will share our
lessons going from experimentation to working prototype, and how to mitigate
failure modes in the production environment. Our work represents one of the
first examples of an industrial-scale solution for compatibility-based fashion
recommendation.

本篇论文介绍了如何使用基于样式兼容性的完整外观推荐系统 Complete The Look (CTL) ， 并使用超过 1 百万套装和 4 百万商品的外观数据集进行训练。我们描述了评估这个主观任务的方法以及在多个训练方法之间比较模型性能。

Pinterest 完善搭配展示技术

Bootstrapping Complete The Look at Pinterest

Humans develop a common sense of style compatibility between items based on
their attributes. We seek to automatically answer questions like "Does this
shirt go well with that pair of jeans?" In order to answer these kinds of
questions, we attempt to model human sense of style compatibility in this
paper. The basic assumption of our approach is that most of the important
attributes for a product in an online store are included in its title
description. Therefore it is feasible to learn style compatibility from these
descriptions. We design a Siamese Convolutional Neural Network architecture and
feed it with title pairs of items, which are either compatible or incompatible.
Those pairs will be mapped from the original space of symbolic words into some
embedded style space. Our approach takes only words as the input with few
preprocessing and there is no laborious and expensive feature engineering.

本文使用孪生卷积神经网络大力提取商品名中的关键属性，以此学习人类的样式兼容性，实现自动化的搭配推荐。