  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.
