Transparent Music Preference Modeling and Recommendation with a Model of Human Memory Theory

In this chapter, we discuss how to utilize human memory models for the task of modeling music preferences for recommender systems. Therefore, we discuss the theoretical underpinnings of using cognitive models for user modeling and recommender systems in order to introduce a model based on the cognitive architecture ACT-R to predict the music genre preferences of users in the Last.fm platform. By implementing the declarative memory module of ACT-R, comprising past usage frequency and recency, as well as the current semantic context, we model the music relistening behavior of users. We evaluate our approach using three user groups that we identify in Last.fm, namely (i) low-mainstream music listeners, (ii) medium-mainstream music listeners, and (iii) high-mainstream music listeners. We find that our approach provides significantly higher prediction accuracy than various baseline algorithms for all three user groups, and especially for the low-mainstream user group. Since our approach is based on a well-established human memory model, we also discuss how this contributes to the transparency of the calculated predictions.