上下起伏：用单类协同过滤模型模拟时尚趋势的视觉演变
Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
摘要：
建立一个成功的推荐系统依赖于理解人们的偏好以及他们的动态维度。在某些领域，例如时尚，由于需要同时对产品的视觉外观及其随时间的演变进行建模，因此对此类偏好进行建模可能非常困难。时尚演变的微妙语义和非线性动力学提出了独特的挑战，特别是考虑到底层数据集的稀疏性和大规模性。在本文中，我们建立了新的模型的一类协同过滤设置，我们的目标是估计用户的时尚感知个性化排名功能的基础上，他们过去的反馈。为了揭示人们在评估产品时考虑的复杂和不断变化的视觉因素，我们的方法结合了从深度卷积神经网络中提取的高级视觉特征，用户过去的反馈以及社区内不断变化的趋势。在实验中，我们在来自Amazon.com的两个大型真实世界数据集上评估了我们的方法，在那里我们展示了它优于最先进的个性化排名措施，并使用它来可视化我们数据集11年跨度的高级时尚趋势。
英文摘要：
Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underlying datasets. In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. To uncover the complex and evolving visual factors that people consider when evaluating products, our method combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community. Experimentally we evaluate our method on two large real-world datasets from Amazon.com, where we show it to outperform state-of-the-art personalized ranking measures, and also use it to visualize the high-level fashion trends across the 11-year span of our dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
