论文标题
使用视觉偏好建模解决服装推荐中的冷门问题
Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling
论文作者
论文摘要
随着时尚界的全球转型以及全球对时尚项目需求的上升，对有效的时尚建议的需求从未如此。尽管过去提出了各种尖端解决方案，以促进时尚的个性化建议，但该技术仍受到其在新实体上的性能不佳的限制，即寒冷的问题。在本文中，我们试图通过利用一组少量输入图像的新型视觉偏好建模方法来解决新用户的冷门问题。我们证明了我们的方法与功能加权聚类的使用来个性化面向场合的服装建议。从数量上讲，我们的结果表明，在服装属性预测方面，提出的视觉偏好模型方法优于最终的最新水平。从定性上讲，通过一项试点研究，我们证明了系统在冷启动场景中提供多样化和个性化建议的功效。
With the global transformation of the fashion industry and a rise in the demand for fashion items worldwide, the need for an effectual fashion recommendation has never been more. Despite various cutting-edge solutions proposed in the past for personalising fashion recommendation, the technology is still limited by its poor performance on new entities, i.e. the cold-start problem. In this paper, we attempt to address the cold-start problem for new users, by leveraging a novel visual preference modelling approach on a small set of input images. We demonstrate the use of our approach with feature-weighted clustering to personalise occasion-oriented outfit recommendation. Quantitatively, our results show that the proposed visual preference modelling approach outperforms state of the art in terms of clothing attribute prediction. Qualitatively, through a pilot study, we demonstrate the efficacy of our system to provide diverse and personalised recommendations in cold-start scenarios.
