Context-aware Music Recommendation
At the moment we are facing a fundamental change in the way people consume music: More and more people switch from private, mostly limited music collections to public music streaming collections containing several millions of tracks generating tons of data. The usability of such streaming services heavily relies on good recommender systems assisting users in discovering music they like. This makes the field of music recommendation and music information retrieval in a highly interesting topic for academia as well as industry. The DBIS Team focuses on context-aware music recommendation, exploiting data sources as Twitter, last.fm. or Spotify. Our research is concerned two types of context: Firstly, we focus on the current activity of a user while listening to music. Secondly, we are concerned with the cultural embedding of a user.
In this research project we analyze music listening behavior using machine learning techniques. The generated insights are integrated into music recommender systems, aiming at improving their prediction accuracy.
#nowplaying is a data set which leverages Twitter for the creation of a diverse and constantly updated data set describing the music listening behavior of users. Twitter is frequently facilitated to post which music the respective user is currently listening to. From such tweets, we extract track and artist information and further metadata. You can find the website for our #nowplaying dataset at dbis-nowplaying.uibk.ac.at.