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Characterisation of explicit feedback in an online music recommendation service
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Most of the approaches for understanding user preferences or taste are based on having explicit feedback from users. However, in many real-life situations we need to rely on implicit feedback. To analyze the relation between implicit and explicit feedback, we conduct a user experiment in the music domain. We find that there is a strong relation between implicit feedback and ratings. We analyze the effect of context variables on the ratings and find that recentness of interaction has a significant effect. We also analyze several user variables. Finally, we propose a simple linear model that relates these variables to the rating we can expect to an item. Such mapping would allow to easily adapt any existing approach that uses explicit feedback to the implicit case and combine both kinds of feedback.