scholarly journals Comparison of implicit and explicit feedback from an online music recommendation service

Author(s):  
Gawesh Jawaheer ◽  
Martin Szomszor ◽  
Patty Kostkova
Author(s):  
Zehra Cataltepe ◽  
Berna Altinel

As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative, content-based and case-based recommendation systems and their hybrids have been used for music recommendation. There are already a number of online music recommendation systems. Although specific user information, such as, demographic data, education, and origin have been shown to affect music preferences, they are usually not collected by the online music recommendation systems, because users would not like to disclose their personal data. Therefore, user models mostly contain information about which music pieces a user liked and which ones s/he did not and when.


2018 ◽  
Vol 43 (2) ◽  
pp. 259-281
Author(s):  
Benjamin Krämer

Abstract The framework of the ‘social ontology of the internet’ is applied to music recommendation platforms. Those websites provide individual suggestions of music to users, creating new dynamics of taste that are no longer based on human-to-human interaction and verbalized judgments. An exemplary analysis of three platforms shows that different conceptions of musical tastes are represented by technical systems: situational emotional preferences, a formalist aesthetics, and social proximity based on tastes. The platforms share certain assumptions about the ontology of musical entities and of course the constitutive act of recommending. We discuss how this act can be ascribed to technical systems. Theses on the platforms’ effects on the social structure of musical tastes are developed.


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