Exploiting Sequential Music Preferences via Optimisation-Based Sequencing

Users in music streaming platforms typically consume tracks sequentially in sessions by interacting with personalised playlists. To satisfy users, music platforms usually rely on recommender systems that learn users' preferences over individual tracks and rank the tracks within each playlist according to the learned preferences. However, such rankings often do not fully exploit the sequential nature of the users' consumption, which may result in a lower within-a-session consumption. In this paper, we model the sequential within-a-session preferences of users and propose an optimisation-based sequencing approach that allows for optimally incorporating such preferences into the rankings. To this end, we rely on interaction data of a major music streaming service to identify two most common aspects of the users' sequential preferences: (1) Position-Aware preferences, and (2) Local-Sequential preferences. We propose a sequencing model that can leverage each of these aspects optimally to maximise the expected total consumption from the session. We further perform an extensive offline and off-policy evaluation of our model, and carry out a large scale online randomised control trial with 7M users across 80 countries. Our findings confirm that we can effectively incorporate sequential preferences of users into our sequencer to make users complete more and skip less tracks within their listening sessions.