Combining insights from the psychology and sociology of music, this article addresses the extent to which the listening behaviour of young adults differs between men and women with regard to (1) preferred music genres, (2) gender of music... more
Combining insights from the psychology and sociology of music, this article addresses the extent to which the listening behaviour of young adults differs between men and women with regard to (1) preferred music genres, (2) gender of music acts and (3) omnivorousness. Drawing on innovative data from the music-based social networking site Last.fm, the analyses show that young females listen more often to ‘softer’ and more mainstream music genres, to female acts and to a wider range of genres than males. Furthermore, by looking into actual listening behaviour and user-generated genre classifications – instead of self-reported preferences – this study contributes to research on gender stratification in music taste, showing that within male-dominated genres young women have carved out particular feminized niches, that women’s pro-female bias is stronger within male-dominated music genres and that the gender difference in omnivorousness disappears when we examine only respondents’ favourite music genres.
The aim of this master thesis is to explore the logic of online personalization and define how it has been applied for music recommendation taking in consideration the complex nature of music. Moreover, through the analysis of several... more
The aim of this master thesis is to explore the logic of online personalization and define how it has been applied for music recommendation taking in consideration the complex nature of music. Moreover, through the analysis of several case studies of recent online music recommender projects (AndroMedia, Lifetrak, Foafing the Music Project, Sourcetone Project and Musicology), this paper attempts to identify the approach that emerging trends are adopting to improve music personalization thanks to context-based and affective recommendations.
Billions of USD are invested in new artists and songs by the music industry every year. This research provides a new strategy for assessing the hit potential of songs, which can help record companies support their investment decisions. A... more
Billions of USD are invested in new artists and songs by the music industry every year. This research provides a new strategy for assessing the hit potential of songs, which can help record companies support their investment decisions. A number of models were developed that use both audio data, and a novel feature based on social media listening behaviour. The results show that models based on early adopter behaviour perform well when predicting top 20 dance hits.
