Escape the bubble: guided exploration of music preferences for serendipity and novelty

Authors:
Maria Taramigkou;Efthimios Bothos;Konstantinos Christidis;Dimitris Apostolou;Gregoris Mentzas
Affiliations:
Institute of Communication and Computer Systems, Athens, Greece;Institute of Communication and Computer Systems, Athens, Greece;Institute of Communication and Computer Systems, Athens, Greece;University of Piraeus, Piraeus, Greece;Institute of Communication and Computer Systems, Athens, Greece
Venue:
Proceedings of the 7th ACM conference on Recommender systems
Year:
2013

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Abstract

In order to predict user behaviour recommender systems generate views of the world according to expressed and known user preferences resulting in 'filter bubbles'. This kind of bubbles generally help users to easily identify objects they like. However, it is becoming increasingly difficult for users to escape their personalized world and change their perspectives especially in domains such as music. In this work we present a methodology and related system that allows users to initiate explorations of music genres by taking a gradual path towards the desired genre while viewing the preferences of other users. The proposed methodology is based on identifying 'latent genres' and using user preference graphs for detecting optimal paths towards a selected target latent genre. In this process we generate suggestions of artists a user should listen to, aiming towards serendipitous and novel encounters. We have implemented our approach in a music recommendation system and evaluated it with encouraging results.