The recent surge of interest in brain-based learning algorithms has improved artificial "sensory" capabilities including image recognition and speech processing, and "motor" activities as with robotics and navigation. A focused handle for addressing the "cognitive" capabilities that might intermediate between the two is recommendations, which deals with how to select between numerous available options based on underlying data, with consideration to desired effects. Brain-based learning algorithms are now beginning to meaningfully impact results here, and in turn benefit from original research in this area. We will review major approaches that are improving recommendations, including deep learning, Bayesian methods, reinforcement learning, and how to evaluate and ensemble them, in data-rich and "cold start" conditions alike. This is an exciting time for developing recommendations that really work, and for exploring cognitive technologies in general, when cleaner and more ubiquitous data can begin to inform more meaningful decisions.