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The traditional recommendation framework seeks to connect user and content,by finding the best match possible based on users past interaction. However, agood content recommendation is not necessarily similar to what the user haschosen in the past. As humans, users naturally evolve, learn, forget, getbored, they change their perspective of the world and in consequence, of therecommendable content. One well known mechanism that affects user interest isthe Mere Exposure Effect: when repeatedly exposed to stimuli, users' interesttends to rise with the initial exposures, reaching a peak, and graduallydecreasing thereafter, resulting in an inverted-U shape. Since previousresearch has shown that the magnitude of the effect depends on a number ofinteresting factors such as stimulus complexity and familiarity, leveragingthis effect is a way to not only improve repeated recommendation but to gain amore in-depth understanding of both users and stimuli. In this work we present(Mere) Exposure2Vec (Ex2Vec) our model that leverages the Mere Exposure Effectin repeat consumption to derive user and item characterization and track userinterest evolution. We validate our model through predicting future musicconsumption based on repetition and discuss its implications for recommendationscenarios where repetition is common.

Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect