Modelling music selection in everyday life with applications for psychology-informed music recommender systems

Music is a highly functional and utilitarian resource. It enables people to regulate emotions, reduce distractions, stimulate physical action, and connect with others. However, with technologically facilitated ubiquitous listening now commonplace, new problems have emerged. The main problem is that of choice: how, given millions of songs to choose from, should providers curate listening experiences? To resolve this, many online platforms employ recommender systems, and there have been concerted efforts to orientate these systems in such a way that they are responsive to the short-term, dynamic needs of listeners in everyday situations. However, there is increasing scrutiny around the impact of automated recommender systems in terms of interpretability and data usage. To this end, researchers have begun exploring ways of integrating knowledge about user behaviours into the recommendation process, rather than through purely data-driven approaches. This thesis aims to bridge these strands of intrigue by exploring an approach to generating situationally determined recommendations, based on an understanding of how and why contextual factors influence music selection in everyday life. This is achieved through three studies, in which contexts, functions, and content of listeners’ music selections are triangulated to make inferences and estimates of situationally congruent musical characteristics. Firstly, a psychometric structure of the functions of music listening is generated. Secondly, this is triangulated with contextual factors and audio features of music selection. Finally, this is supplemented with an exploratory approach to generating recommendations through the explanatory model. These three studies result in both: a preliminary model of goal-orientated music listening that can be deployed by recommender procedures; and provides an exemplar methodology of how to construct behavioural models that can drive such systems. This thesis therefore holds relevance to both psychological research and those interested in music curation techniques.