Context-aware Music Recommendation

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.

Team

Current Theses

Publications

2017

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Martin Pichl, Eva Zangerle and Günther Specht: Improving Context-Aware Music Recommender Systems: Beyond the Pre-filtering Approach. In Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pages 201-208. ACM, 2017

Bib Link

Martin Pichl, Eva Zangerle and Günther Specht: Understanding User-curated Playlists on Spotify: A Machine Learning Approach. In International Journal of Multimedia Data Engineering and Management IJMDEM, vol. 8, no. 4. 2017

2016

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Martin Pichl, Eva Zangerle and Günther Specht: Understanding Playlist Creation on Music Streaming Platforms. In Proceedings of the IEEE Symposium on Multimedia (ISM). IEEE, 2016.

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Eva Zangerle, Martin Pichl, Benedikt Hupfauf and Günther Specht: Can Microblogs Predict Music Charts? An Analysis of the Relationship Between #Nowplaying Tweets and Music Charts. In Proceedings of the 17th International Society for Music Information Retrieval Conference, ISMIR 2016, New York City, United States, August 7-11, 2016, pages 365-371. ISMIR, 2016.

2015

Bib Link

Martin Pichl, Eva Zangerle and Günther Specht: Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?. In 15th IEEE International Conference on Data Mining Workshops (ICDM 2016), pages 1360-1365. IEEE, 2015.

Bib Link

Martin Pichl, Eva Zangerle and Günther Specht: #nowplaying on #Spotify: Leveraging Spotify Information on Twitter for Artist Recommendations. In Current Trends in Web Engineering, 15th International Conference, ICWE 2015 Workshops (Revised Selected Papers), pages 163-174. Springer, 2015.

2014

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Martin Pichl, Eva Zangerle and Günther Specht: Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation. In Proceedings of the 26nd Workshop Grundlagen von Datenbanken (GvDB 2014), Ritten, Italy, vol. 1313, pages 35-40. CEUR-WS.org, oct 2014.

Bib Link Download

Eva Zangerle, Martin Pichl, Wolfgang Gassler and Günther Specht: #nowplaying Music Dataset: Extracting Listening Behavior from Twitter. In Proceedings of the 1st ACM International Workshop on Internet-Scale Multimedia Management, pages 21-26. ACM, June 2014.

2012

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Eva Zangerle, Wolfgang Gassler and Günther Specht: Exploiting Twitter's Collective Knowledge for Music Recommendations. In Proceedings, 2nd Workshop on Making Sense of Microposts (#MSM2012): Big things come in small packages, Lyon, France, 16 April 2012 (in connection with the 21st International Conference on World Wide Web), pages 14-17. 2012