Summary of approach: they embed the photos (convert photo->vector of numbers using T-SNE or CNNs [the details are actually here https://devblogs.nvidia.com/para

Summary of approach: they embed the photos (convert photo->vector of numbers using T-SNE or CNNs [the details are actually here https://devblogs.nvidia.com/parallelforall/understanding-aesthetics-deep-learning/] https://devblogs.nvidia.com/parallelforall/understanding-aes...) and then train a small-ish classifier (three fully connected layers) on top of it to capture a user's preference. A pretty obvious approach, basic version should be doable in a hackathon, but cool result nonetheless. "We chose a three-layer multilayer perceptron (MLP) network as a good ranker, since it is able to capture the inherent non-linear shift in distribution between the user’s choices and the original training set. Notably, an MLP can be trained rapidly by leveraging GPU computation to obtain near-real-time results. This is important because it enables us to build interactive interface, as we’ll explain. We typically precompute a set of negative features (about 40,000 negative samples) and extract the positive features from the user-provided input."