  Providing suitable recommendations is of vital importance to improve the user
satisfaction of music recommender systems. Here, users often listen to the same
track repeatedly and appreciate recommendations of the same song multiple
times. Thus, accounting for users' relistening behavior is critical for music
recommender systems. In this paper, we describe a psychology-informed approach
to model and predict music relistening behavior that is inspired by studies in
music psychology, which relate music preferences to human memory. We adopt a
well-established psychological theory of human cognition that models the
operations of human memory, i.e., Adaptive Control of Thought-Rational (ACT-R).
In contrast to prior work, which uses only the base-level component of ACT-R,
we utilize five components of ACT-R, i.e., base-level, spreading, partial
matching, valuation, and noise, to investigate the effect of five factors on
music relistening behavior: (i) recency and frequency of prior exposure to
tracks, (ii) co-occurrence of tracks, (iii) the similarity between tracks, (iv)
familiarity with tracks, and (v) randomness in behavior. On a dataset of 1.7
million listening events from Last.fm, we evaluate the performance of our
approach by sequentially predicting the next track(s) in user sessions. We find
that recency and frequency of prior exposure to tracks is an effective
predictor of relistening behavior. Besides, considering the co-occurrence of
tracks and familiarity with tracks further improves performance in terms of
R-precision. We hope that our work inspires future research on the merits of
considering cognitive aspects of memory retrieval to model and predict complex
user behavior.
