A Web System Based on Spotify for the automatic generation of affective playlists

Abstract

The online music streaming providers offer powerful personalization tools for recommending songs to their registered users. These tools are usually based on users’ listening histories and tastes, but ignore other contextual variables that affect users while listening to music, for example, the user’s mood. In this paper, a Web-based system for generating affective playlists that regulate the user’s mood is presented. The system has been implemented integrating resources and data offered by Spotify through its service platform, and the playlists generated are directly published in the user’s Spotify account. Internally, the emotions play a relevant role in the processes of cataloguing songs and making personalized music recommendations. Novel affective computing solutions are combined with traditional information retrieval and artificial intelligence techniques in order to solve these complex engineering problems. Besides, these solutions consider users’ collaboration as a first-class element in an attempt to improve affective recommendations.