The rapid growth of the online fashion market brought demands for innovative
fashion services and commerce platforms. With the recent success of deep
learning, many applications employ AI technologies such as visual search and
recommender systems to provide novel and beneficial services. In this paper, we
describe applied technologies for AI-driven fashion social networking service
that incorporate fashion e-commerce. In the application, people can share and
browse their outfit-of-the-day (OOTD) photos, while AI analyzes them and
suggests similar style OOTDs and related products. To this end, we trained deep
learning based AI models for fashion and integrated them to build a fashion
visual search system and a recommender system for OOTD. With aforementioned
technologies, the AI-driven fashion SNS platform, iTOO, has been successfully
launched.

本文介绍了一种采用深度学习技术的 AI 驱动时尚社交网络服务，包括时尚电子商务，人们可以分享和浏览他们的 OOTD 照片，而 AI 分析它们并建议类似的风格 OOTD 和相关产品。

基于人工智能的时尚社交电商技术

Technologies for AI-Driven Fashion Social Networking Service with E-Commerce

With the rapid growth of online fashion market, demand for effective fashion
recommendation systems has never been greater. In fashion recommendation, the
ability to find items that goes well with a few other items based on style is
more important than picking a single item based on the user's entire purchase
history. Since the same user may have purchased dress suits in one month and
casual denims in another, it is impossible to learn the latent style features
of those items using only the user ratings. If we were able to represent the
style features of fashion items in a reasonable way, we will be able to
recommend new items that conform to some small subset of pre-purchased items
that make up a coherent style set. We propose Style2Vec, a vector
representation model for fashion items. Based on the intuition of
distributional semantics used in word embeddings, Style2Vec learns the
representation of a fashion item using other items in matching outfits as
context. Two different convolutional neural networks are trained to maximize
the probability of item co-occurrences. For evaluation, a fashion analogy test
is conducted to show that the resulting representation connotes diverse fashion
related semantics like shapes, colors, patterns and even latent styles. We also
perform style classification using Style2Vec features and show that our method
outperforms other baselines.

提出了一种名为 Style2Vec 的表示模型，使用卷积神经网络学习时尚产品的风格特征，以较小的前购买物品集合作为一致的风格设置的子集，具有较好的时尚分类性能。