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 first repetitions and attains a peak after which interest will decrease with subsequent exposures, 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 recommendation to derive user and item characterization and track user interest over multiple repetitions. We validate our model through predicting future music consumption based on repetition and discuss its implications for recommendation scenarios where repetition is common.
This paper has been accepted for publication in the proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023).
It will be available online soon.