  In many image-related tasks, learning expressive and discriminative
representations of images is essential, and deep learning has been studied for
automating the learning of such representations. Some user-centric tasks, such
as image recommendations, call for effective representations of not only images
but also preferences and intents of users over images. Such representations are
termed \emph{hybrid} and addressed via a deep learning approach in this paper.
We design a dual-net deep network, in which the two sub-networks map input
images and preferences of users into a same latent semantic space, and then the
distances between images and users in the latent space are calculated to make
decisions. We further propose a comparative deep learning (CDL) method to train
the deep network, using a pair of images compared against one user to learn the
pattern of their relative distances. The CDL embraces much more training data
than naive deep learning, and thus achieves superior performance than the
latter, with no cost of increasing network complexity. Experimental results
with real-world data sets for image recommendations have shown the proposed
dual-net network and CDL greatly outperform other state-of-the-art image
recommendation solutions.
