Beyond User Needs: A Meaning-Oriented Approach to Recommender Systems

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This contribution is a case study of Spotify, a popular music streaming app, which uses automated recommendations to provide a better user experience to its listeners. Automated recommender systems have mostly been built around understanding user needs and user goals. Our case study presents a meaning-oriented approach aimed at understanding what users regard as meaningful and how an automated recommender system can forge meaning and offer experiences that help develop existing connections to music and generate new ones.

Following the meaning-oriented approach inspired by Lucien Karpik (2010), we were able to better understand how different audience segments engage with music and experience music as meaningful. We identified 2 cultural engagement models that listeners use to relate to music: (1) musical engagement during which music is the focus of the experience; and (2) non-musical engagement, during which the listener is the focus of the experience. Each engagement model uses different types of cognitive and evaluative aids, which we refer to as cues and proof points, to derive meaning from listening experiences. We also identified nine distinct types of experiences of meaning defined by distinct types of cues and proof points.

The proposed approach is applicable to the study and innovation of experience-led digital platforms and recommender systems.

Keywords: meaning, recommender systems, music, streaming

Article citation: 2020 EPIC Proceedings pp 191–202, ISSN 1559-8918,

Pages: 1 2 3

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