While awe-inspiring and inflammatory, Gary is very clear: hustling sucks. Working hard sucks. Not going to the beach, not staying out at bars, not watching Netflix all suck. But you know what sucks more? When you are 85 and filled with regret. Regret that you never started that business. Regret that you didn’t write that novel. Regret that you didn’t finish college.
Given a user u and an item i, we calculate a score that serves as a proxy for preference. This means that high scores mirror high preference and vice versa. This score allows us to rank a set of items according to their relevance for a given user. The score can be a user’s probability to interact with an item. Thus, we aim to predict user-item interaction probabilities. To compute this probability, we use a deep neural network with a single output unit. This unit uses the sigmoid function for activation, which yields output values in the interval (0, 1). Thus, we can interpret the network output as probability and use it for ranking. The network is then trained on distinguishing between preference and disregard. Therefore, we label all positive user-item combinations with 1 and negatives with 0. As a result, the learning task presents itself as a binary classification task. Excelling at this task can enhance the relevance of our recommendations.