Textile pattern design is a time-consuming and tedious work. Mihui, our ongoing developing system, employs deep-learning techniques to automatically generate huge volumes of patterns with the help of human guidance. However, trained as a black box, Mihui cannot provide customized service for each individual designer who shows unique aesthetics preferences. In this article, we introduce the recommendation module of Mihui. The module forwards all generated pattern images to a deep encoding network, where images are mapped into 128-dimension vectors. For each user of Mihui, we create a profile by his/her purchased or downloaded history. A novel encoder network is proposed to learn a personal taste vector for each user, based on which, we recommend new patterns to him/her. Our records in Mihui show that the recommendation module effectively improve users' experience on Mihui.

中文翻译：

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使用卷积神经网络和自动编码器的纺织品图案推荐

纺织图案设计是一项耗时且繁琐的工作。Mihui 是我们正在开发的系统，它采用深度学习技术，在人类指导的帮助下自动生成大量模式。然而，米灰作为一个黑匣子，无法为每个表现出独特审美偏好的设计师提供定制服务。在这篇文章中，我们介绍一下米灰的推荐模块。该模块将所有生成的模式图像转发到深度编码网络，其中图像被映射为 128 维向量。对于米慧的每个用户，我们都会根据他/她的购买或下载历史记录创建一个个人资料。提出了一种新颖的编码器网络来学习每个用户的个人品味向量，基于此，我们向他/她推荐新的模式。