Personalized implicit learning in a music recommender system

Abstract

Recommender systems typically require feedback from the user to learn the user's taste. This feedback can come in two forms: explicit and implicit. Explicit feedback consists of ratings provided by the user for a number of items, while implicit feedback comes from observing user actions on items. These actions have to be interpreted by the recommender system and translated into a rating. In this paper we propose a method to learn how to translate user actions on items to ratings on these items by correlating user actions with explicit feedback. We do this by associating user actions to rated items and subsequently applying naive Bayesian classification to rate new items with which the user has interacted. We apply and evaluate our method on data from a web-based music service and we show its potential as an addition to explicit rating. © 2010 Springer-Verlag.