The influence of personal values on music taste: Towards value-based music recommendations

The feld of recommender systems has a lot to gain from the feld of psychology. Indeed, many psychology researchers have investigated relations between models that describe humans and consumption preferences. One example of this is personality, which has been shown to be a valid construct to describe people. As a consequence, personality-based recommenders have already proven to be a lead toward improving recommendations, by adapting them to their users' traits. Beyond personality, there are more ways to describe a person's identity. One of these ways is to consider personal values: what is important for the users in life at the most abstract level. Being complementary to personality traits, values may give another lead towards better user understanding. In this paper, we investigate this, taking music as a use case. We use a marketing interview technique to elicit 22 users' personal values connected to their musical preferences. We show that personal values indeed play a role in people's music preferences, and are the frst to propose a map linking personal values to music preferences. We see this map as a frst step in devising a value-based user model for music recommender systems.Multimedia ComputingIntelligent System

Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference

Prior research from the field of music psychology has suggested that there are factors common to music preference beyond individual genres. Specifically, research has shown that self-reported ratings of preference for individual musical genres can be reduced to 4 or 5 dimensions, which in turn have been shown to correlate to relevant psychological constructs, such as personality. However, the number of dimensions emerging from multiple studies has varied despite the care taken in conducting such research. Data-driven approaches offer opportunities to further this line of research with actual listening data, at a scale and scope surpassing that of traditional psychological studies. Although listening data can be considered more direct and comprehensive evidence of listening preference, transforming this data into meaningful measurements is non-trivial. In the current paper, we report on investigations seeking to find interpretable underlying dimensions of music taste, using implicit large-scale listening data. Offering a critical reflection on potential researchers' degrees of freedom, we adopt an explicit systematic approach, investigating the impact of varying different parameters, analysis, and normalization techniques. More precisely, we consider various ways to extract listening preference information from two large, openly available datasets of music listening behavior, making use of principal component analysis and variational autoencoders to extract potential underlying dimensions. Results and implications are discussed in light of prior psychological theory, and the potential of user listening data to further research on music preference.Multimedia Computin