#Nowplaying on #Spotify: Leveraging Spotify Information on Twitter for Artist Recommendations

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9396)

Abstract

The rise of the web enabled new distribution channels like online stores and streaming platforms, offering a vast amount of different products. For helping customers finding products according to their taste on those platforms, recommender systems play an important role. Besides focusing on the computation of the recommendations itself, in literature the problem of a lack of data appropriate for research is discussed. In order to overcome this problem, we present a music recommendation system exploiting a dataset containing listening histories of users, who posted what they are listening to at the moment on the microblogging platform Twitter. As this dataset is updated daily, we propose a genetic algorithm, which allows the recommender system to adopt its input parameters to the extended dataset. In the evaluation part of this work, we benchmark the presented recommender system against two baseline approaches. We show that the performance of our proposed recommender is promising and clearly outperforms the baseline.

Keywords

Music recommender systems Collaborative filtering Social media Twitter 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Databases and Information Systems, Institute of Computer ScienceUniversity of InnsbruckInnsbruckAustria

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