Shifting Consumption towards Diverse Content on Music Streaming Platforms
Christian Hansen, Rishabh Mehrotra, Casper Worm Hansen, Brian Brost, Lucas Maystre, Mounia Lalmas
Abstract
Algorithmic recommendations shape music consumption at scale, and understanding the impact of various algorithmic models on how content is consumed is a central question for music streaming platforms. The ability to shift consumption towards less popular content and towards content different from user's typical historic tastes not only affords the platform ways of handling issues such as filter bubbles and popularity bias, but also contributes to maintaining a healthy and sustainable consumption patterns necessary for overall platform success.
Topics & Concepts
Consumption (sociology)Computer sciencePopularityContent (measure theory)MultimediaScale (ratio)AestheticsArtQuantum mechanicsPhysicsPsychologyMathematical analysisMathematicsSocial psychologyRecommender Systems and TechniquesCaching and Content DeliveryAdvanced Data Storage Technologies