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Introducing global and regional mainstreaminess for improving personalized music recommendation
Markus Schedl, Christine Bauer
Sub Human-Centered Computing
Human-Centered Computing
Johannes Kepler University of Linz
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Dive into the research topics of 'Introducing global and regional mainstreaminess for improving personalized music recommendation'. Together they form a unique fingerprint.
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Keyphrases
Music Recommendation
100%
Preference Profile
100%
Large Population
33%
Radio
33%
Accuracy Improvement
33%
Kullback-Leibler Divergence
33%
Weight Function
33%
Distribution Channels
33%
Symmetrized
33%
Term Frequency-inverse Document Frequency (TF-IDF)
33%
Recommendation Process
33%
Rating Prediction
33%
Listening Preference
33%
Music Listeners
33%
Kendall's Tau Rank Correlation
33%
Arts and Humanities
Artists
100%
Regional
100%
Music Recommendation
100%
Genre
33%
Economics, Econometrics and Finance
Distribution Channel
100%
Kullback-Leibler Divergence
100%
Computer Science
Leibler Divergence
100%
Weighting Functions
100%