  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. As humans, users naturally evolve, learn, forget, get
bored, they change their perspective of the world and in consequence, of the
recommendable content. One well known mechanism that affects user interest is
the Mere Exposure Effect: when repeatedly exposed to stimuli, users' interest
tends to rise with the initial exposures, reaching a peak, and gradually
decreasing thereafter, resulting in an inverted-U shape. Since previous
research has shown that the magnitude of the effect depends on a number of
interesting factors such as stimulus complexity and familiarity, leveraging
this effect is a way to not only improve repeated recommendation but to gain a
more in-depth understanding of both users and stimuli. In this work we present
(Mere) Exposure2Vec (Ex2Vec) our model that leverages the Mere Exposure Effect
in repeat consumption to derive user and item characterization and track user
interest evolution. We validate our model through predicting future music
consumption based on repetition and discuss its implications for recommendation
scenarios where repetition is common.
