publication . Part of book or chapter of book . 2018

Music Recommendations

Jannach, Dietmar; Kamehkhosh, Iman; Bonnin, Geoffray;
Open Access English
  • Published: 01 Jan 2018
  • Publisher: HAL CCSD
  • Country: France
Abstract
International audience; Today's online music services like Spotify provide their listeners with different types of music recommendations, e.g., in the form of weekly recommendations or personalized radio stations. Such recommendations are often based, at least in parts, on collaborative filtering techniques. In this chapter, we first review the different types of music recommendations that can be found in practice and discuss the specific challenges of the domain. Next, we discuss technical approaches for the problems of music discovery and next-track recommendation in more depth, with a specific focus on their practical application at Spotify. Finally, we furth...
Subjects
free text keywords: [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]
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Part of book or chapter of book . 2018
http://dx.doi.org/10.1142/9789...
Part of book or chapter of book
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97 references, page 1 of 7

Adomavicius, G. and Kwon, Y. (2012). Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques, IEEE TKDE 24, 5, pp. 896{911.

Anderson, A., Kumar, R., Tomkins, A., and Vassilvitskii, S. (2014). The dynamics of repeat consumption, in WWW '14, pp. 419{430.

Bachrach, Y., Finkelstein, Y., Gilad-Bachrach, R., Katzir, L., Koenigstein, N., Nice, N., and Paquet, U. (2014). Speeding Up the Xbox Recommender System Using a Euclidean Transformation for Inner-Product Spaces, in RecSys '14, pp. 257{264.

Balkema, W. and van der Heijden, F. (2010). Music Playlist Generation by Assimilating GMMs into SOMs, Pattern Recognition Letters 31, 11, pp. 1396{1402.

Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., Luke, K.-H., and Schwaiger, R. (2011). InCarMusic: Context-Aware Music Recommendations in a Car, in ECWeb '11, pp. 89{100. [OpenAIRE]

Barrington, L., Oda, R., and Lanckriet, G. R. G. (2009). Smarter than Genius? Human Evaluation of Music Recommender Systems, in ISMIR '09, pp. 357{362.

Berkovsky, S. and Freyne, J. (2010). Group-based Recipe Recommendations: Analysis of Data Aggregation Strategies, in RecSys '10, pp. 111{118. [OpenAIRE]

Bernhardsson, E. (2013). Music Recommendations at Spotify, Online https://de.slideshare. net/erikbern/collaborative-filtering-at-spotify-16182818.

Bernhardsson, E. (2014). Recurrent Neural Networks for Collaborative Filtering, Online https://erikbern.com/2014/06/28/ recurrent-neural-networks-for-collaborative-filtering.html.

Bernhardsson, E. (2015). Approximate Nearest Neighbor Methods and Vector Models, Online https://de.slideshare.net/erikbern/ approximate-nearest-neighbor-methods-and-vector-models-nyc-ml-meetup.

Bernhardsson, E. (2017). Conversion Rates - You Are (Most Likely) Computing Them Wrong, Online https://erikbern.com/2017/05/23/ conversion-rates-you-are-most-likely-computing-them-wrong.html.

17See Chau et al. (2013) for a user study on the topic of bad recommendations.

Bertin-Mahieux, T., Ellis, D. P., Whitman, B., and Lamere, P. (2011). The Million Song Dataset, in ISMIR '11, pp. 591{596. [OpenAIRE]

Bieschke, E. (2014). Pandora, presentation at MLconf2013, Online https://de.slideshare.net/SessionsEvents/eric-bieschke-slides.

Bonnin, G. and Jannach, D. (2013). Evaluating the Quality of Generated Playlists Based on Hand-Crafted Samples, in ISMIR '13, pp. 263{268. [OpenAIRE]

97 references, page 1 of 7
Abstract
International audience; Today's online music services like Spotify provide their listeners with different types of music recommendations, e.g., in the form of weekly recommendations or personalized radio stations. Such recommendations are often based, at least in parts, on collaborative filtering techniques. In this chapter, we first review the different types of music recommendations that can be found in practice and discuss the specific challenges of the domain. Next, we discuss technical approaches for the problems of music discovery and next-track recommendation in more depth, with a specific focus on their practical application at Spotify. Finally, we furth...
Subjects
free text keywords: [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]
Download fromView all 3 versions
http://pdfs.semanticscholar.or...
Part of book or chapter of book
Provider: UnpayWall
Hyper Article en Ligne
Part of book or chapter of book . 2018
http://dx.doi.org/10.1142/9789...
Part of book or chapter of book
Provider: Crossref
97 references, page 1 of 7

Adomavicius, G. and Kwon, Y. (2012). Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques, IEEE TKDE 24, 5, pp. 896{911.

Anderson, A., Kumar, R., Tomkins, A., and Vassilvitskii, S. (2014). The dynamics of repeat consumption, in WWW '14, pp. 419{430.

Bachrach, Y., Finkelstein, Y., Gilad-Bachrach, R., Katzir, L., Koenigstein, N., Nice, N., and Paquet, U. (2014). Speeding Up the Xbox Recommender System Using a Euclidean Transformation for Inner-Product Spaces, in RecSys '14, pp. 257{264.

Balkema, W. and van der Heijden, F. (2010). Music Playlist Generation by Assimilating GMMs into SOMs, Pattern Recognition Letters 31, 11, pp. 1396{1402.

Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., Luke, K.-H., and Schwaiger, R. (2011). InCarMusic: Context-Aware Music Recommendations in a Car, in ECWeb '11, pp. 89{100. [OpenAIRE]

Barrington, L., Oda, R., and Lanckriet, G. R. G. (2009). Smarter than Genius? Human Evaluation of Music Recommender Systems, in ISMIR '09, pp. 357{362.

Berkovsky, S. and Freyne, J. (2010). Group-based Recipe Recommendations: Analysis of Data Aggregation Strategies, in RecSys '10, pp. 111{118. [OpenAIRE]

Bernhardsson, E. (2013). Music Recommendations at Spotify, Online https://de.slideshare. net/erikbern/collaborative-filtering-at-spotify-16182818.

Bernhardsson, E. (2014). Recurrent Neural Networks for Collaborative Filtering, Online https://erikbern.com/2014/06/28/ recurrent-neural-networks-for-collaborative-filtering.html.

Bernhardsson, E. (2015). Approximate Nearest Neighbor Methods and Vector Models, Online https://de.slideshare.net/erikbern/ approximate-nearest-neighbor-methods-and-vector-models-nyc-ml-meetup.

Bernhardsson, E. (2017). Conversion Rates - You Are (Most Likely) Computing Them Wrong, Online https://erikbern.com/2017/05/23/ conversion-rates-you-are-most-likely-computing-them-wrong.html.

17See Chau et al. (2013) for a user study on the topic of bad recommendations.

Bertin-Mahieux, T., Ellis, D. P., Whitman, B., and Lamere, P. (2011). The Million Song Dataset, in ISMIR '11, pp. 591{596. [OpenAIRE]

Bieschke, E. (2014). Pandora, presentation at MLconf2013, Online https://de.slideshare.net/SessionsEvents/eric-bieschke-slides.

Bonnin, G. and Jannach, D. (2013). Evaluating the Quality of Generated Playlists Based on Hand-Crafted Samples, in ISMIR '13, pp. 263{268. [OpenAIRE]

97 references, page 1 of 7
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