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.
Transformers emerged as powerful methods for sequential recommendation. However, existing architectures often overlook the complex dependencies between user preferences and the temporal context. In this short paper, we introduce MOJITO, an improved Transformer sequential recommender system that addresses this limitation. MOJITO leverages Gaussian mixtures of attention-based temporal context and item embedding representations for sequential modeling. Such an approach permits to accurately predict which items should be recommended next to users depending on past actions and the temporal context. We demonstrate the relevance of our approach, by empirically outperforming existing Transformers for sequential recommendation on several real-world datasets.
Repetition in music consumption is a common phenomenon. It is notably more frequent when compared to the consumption of other media, such as books and movies. In this paper, we show that one particularly interesting repetitive behavior arises when users are consuming new items. 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. This behavior, which has been extensively studied in psychology, is called the mere exposure effect. In this paper, we show how a number of factors, both content and user-based, well documented in the literature on the mere exposure effect, modulate the magnitude of the effect. Due to the vast availability of data of users discovering new songs everyday in music streaming platforms, these findings enable new ways to characterize both the music, users and their relationships. Ultimately, it opens up the possibility of developing new recommender systems paradigms based on these characterizations.