Building effective recommender systems for domains like fashion is challenging due to the high level of subjectivity and the semantic complexity of the features involved (i.e., fashion styles). Recent work has shown that approaches to `visual' recommendation (e.g.~clothing, art, etc.) can be made more accurate by incorporating visual signals directly into the recommendation objective, using `off-the-shelf' feature representations derived from deep networks. Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i.e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features. Furthermore, we show that our model can be used \emph{generatively}, i.e., given a user and a product category, we can generate new images (i.e., clothing items) that are most consistent with their personal taste. This represents a first step towards building systems that go beyond recommending existing items from a product corpus, but which can be used to suggest styles and aid the design of new products.

本文研究了在时尚等领域中建立有效的推荐系统的挑战，并提出了一种基于Siamese CNNs以及深度学习网络的时尚感知图像表示和推荐方法，通过在像素级别训练图像表示和推荐系统，可以显著提高推荐性能和生成新的时尚产品设计。

基于生成图像模型的视觉感知时尚推荐与设计