Music recommendation engines play a pivotal role in connecting artists with their listeners. Optimizing myopically only for user satisfaction may lead systems to recommend just a small fraction of all the available artists, or to recommend artists to users who might engage with them only in the short-term. In this work, we investigate such effects by exploring different signals of implicit feedback provided by users when using a music service (i.e., counting the number of tracks, days or times a ...
Music recommendation engines play a pivotal role in connecting artists with their listeners. Optimizing myopically only for user satisfaction may lead systems to recommend just a small fraction of all the available artists, or to recommend artists to users who might engage with them only in the short-term. In this work, we investigate such effects by exploring different signals of implicit feedback provided by users when using a music service (i.e., counting the number of tracks, days or times a user listens to an artist) and propose novel combined signals. Our approaches are evaluated over four different datasets, combining traditional user-centered evaluation metrics with artist-based ones, which allows us to measure the quality of the recommendations and the potential engagement with the recommended artists. Our experiments reveal that the selection of the implicit feedback signal has a significant impact on the quality of the recommendations. In addition, we show that the proposed signals increase the chances of a higher engagement between users and the artists they get recommended.
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