The traditional recommendation framework seeks to connect user and content, by finding the best match possible based on users past interaction. However, a good content recommendation is not necessarily similar to what the user has chosen in the past. One limitation of basing future interaction on what happened in the past is that it ignores the fact that both sides of the problems are dynamic. As human, users naturally evolve, learn, forget, get bored, they change their perspective of the world and in consequence, of the recommendable content. In this work we present Ex2Vec our framework for accounting to the dynamic of the human side of the recommendation problem. We introduce the Mere Exposure Effect as a common phenomenon in music streaming platforms. We then present our model that leverage the effect for jointly characterizing users and music. We validate our model through predicting future music consumption based on repetition and discuss its implications.