E-commerce platforms surface interesting products largely through product
recommendations that capture users' styles and aesthetic preferences. Curating
recommendations as a complete complementary set, or assortment, is critical for
a successful e-commerce experience, especially for product categories such as
furniture, where items are selected together with the overall theme, style or
ambiance of a space in mind. In this paper, we propose two visually-aware
recommender systems that can automatically curate an assortment of living room
furniture around a couple of pre-selected seed pieces for the room. The first
system aims to maximize the visual-based style compatibility of the entire
selection by making use of transfer learning and topic modeling. The second
system extends the first by incorporating text data and applying polylingual
topic modeling to infer style over both modalities. We review the production
pipeline for surfacing these visually-aware recommender systems and compare
them through offline validations and large-scale online A/B tests on Overstock.
Our experimental results show that complimentary style is best discovered over
product sets when both visual and textual data are incorporated.

本文提出了两种可视化推荐系统，该系统可以自动为客厅家具配套筛选整个选择，以最大化配合效果和利用文本数据和多语言话题建模来推断风格。实验结果表明，从包括视觉和文本数据的产品集合中发现补充风格效果最好。