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Found 5 papers, 3 papers with code
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
In this paper, we introduce PISA (Psychology-Informed Session embedding using ACT-R), a session-level sequential recommender system that overcomes this limitation.
Modeling Activity-Driven Music Listening with PACE
While the topic of listening context is widely studied in the literature of music recommender systems, the integration of regular user behavior is often omitted.
Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect
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.
Attention Mixtures for Time-Aware Sequential Recommendation
Transformers emerged as powerful methods for sequential recommendation.
Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization
Repetition in music consumption is a common phenomenon.
