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.

本文介绍了一种利用深度卷积神经网络，结合用户反馈和时尚趋势进行实时热门商品推荐的算法，以及该算法在 Amazon 上进行的实验。

上下：用单类协同过滤模型建模时尚趋势的视觉演化

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with  One-Class Collaborative Filtering

Imagery offers a rich description of our world and communicates a volume and
type of information that cannot be captured by text alone. Since the invention
of the camera, an ever-increasing number of photographs document our "visual
culture" complementing historical texts. But currently, this treasure trove of
knowledge can only be analyzed manually by historians, and only at small scale.
In this paper we perform automated analysis on a large-scale historical image
dataset. Our main contributions are: 1) A publicly-available dataset of 168,055
(37,921 frontal-facing) American high school yearbook portraits. 2)
Weakly-supervised data-driven techniques to discover historical visual trends
in fashion and identify date-specific visual patterns. 3) A classifier to
predict when a portrait was taken, with median error of 4 years for women and 6
for men. 4) A new method for discovering and displaying the visual elements
used by the CNN-based date-prediction model to date portraits, finding that
they correspond to the tell-tale fashions of each era. Project page can be
found at:
this http URL .

该研究通过对大规模历史图像数据集进行自动化分析，发现历史时尚的视觉趋势并识别与日期相关的视觉模式，同时开发了一个分类器以预测肖像照片的拍摄时间。

百年肖像：美国高中年鉴的视觉历史记录

A Century of Portraits: A Visual Historical Record of American High  School Yearbooks

We describe a completely automated large scale visual recommendation system
for fashion. Our focus is to efficiently harness the availability of large
quantities of online fashion images and their rich meta-data. Specifically, we
propose four data driven models in the form of Complementary Nearest Neighbor
Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain
LDA for solving this problem. We analyze relative merits and pitfalls of these
algorithms through extensive experimentation on a large-scale data set and
baseline them against existing ideas from color science. We also illustrate key
fashion insights learned through these experiments and show how they can be
employed to design better recommendation systems. Finally, we also outline a
large-scale annotated data set of fashion images (Fashion-136K) that can be
exploited for future vision research.

本文提出了四个数据驱动模型用于大规模的视觉推荐系统，这些模型可以有效地利用大量的时尚图片及其丰富的元数据，通过对大规模数据集的深入实验对这些算法进行了分析，并在颜色科学的基础上对其进行了基线，同时展示了从这些实验中学到的关键时尚见解并介绍了一个可以用于未来视觉研究的大规模时尚图像注释数据集（Fashion-136K）。