  Creative plays a great important role in e-commerce for exhibiting products.
Sellers usually create multiple creatives for comprehensive demonstrations,
thus it is crucial to display the most appealing design to maximize the
Click-Through Rate~(CTR). For this purpose, modern recommender systems
dynamically rank creatives when a product is proposed for a user. However, this
task suffers more cold-start problem than conventional products recommendation
In this paper, we propose a hybrid bandit model with visual priors which first
makes predictions with a visual evaluation, and then naturally evolves to focus
on the specialities through the hybrid bandit model. Our contributions are
three-fold: 1) We present a visual-aware ranking model (called VAM) that
incorporates a list-wise ranking loss for ordering the creatives according to
the visual appearance. 2) Regarding visual evaluations as a prior, the hybrid
bandit model (called HBM) is proposed to evolve consistently to make better
posteriori estimations by taking more observations into consideration for
online scenarios. 3) A first large-scale creative dataset, CreativeRanking, is
constructed, which contains over 1.7M creatives of 500k products as well as
their real impression and click data. Extensive experiments have also been
conducted on both our dataset and public Mushroom dataset, demonstrating the
effectiveness of the proposed method.

Computer Vision and Pattern Recognition;Information Retrieval