scholarly journals Multidimensional Optimization Model of Music Recommender Systems

2012 ◽  
Vol 19B (3) ◽  
pp. 155-164 ◽  
Author(s):  
Kyong-Su Park ◽  
Nam-Me Moon
Keyword(s):  
Recommender Systems ◽  
Optimization Model ◽  
Music Recommender Systems ◽  
Multidimensional Optimization

scholarly journals Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems

2015 ◽  
Vol 219 ◽  
pp. 25-39 ◽  
Author(s):  
Ben Horsburgh ◽  
Susan Craw ◽  
Stewart Massie
Keyword(s):  
Recommender Systems ◽  
Music Recommender Systems

Evaluating Hybrid Music Recommender Systems

2013 ◽  
Author(s):  
Thomas Hornung ◽  
Cai-Nicolas Ziegler ◽  
Simon Franz ◽  
Martin Przyjaciel-Zablocki ◽  
Alexander Schatzle ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Music Recommender Systems

A Study about The Impact of Music Recommender Systems on Online Digital Music Rankings

2014 ◽  
Vol 16 (3) ◽  
pp. 49-68
Author(s):  
HyunMo Kim ◽  
◽  
MinYong Kim ◽  
JaeHong Park
Keyword(s):  
Recommender Systems ◽  
Digital Music ◽  
Music Recommender Systems ◽  
The Impact

scholarly journals Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?

2021 ◽  
Author(s):  
Oleg Lesota ◽  
Alessandro Melchiorre ◽  
Navid Rekabsaz ◽  
Stefan Brandl ◽  
Dominik Kowald ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Music Recommender Systems

scholarly journals Exploiting Music Play Sequence for Music Recommendation

2017 ◽  
Author(s):  
Zhiyong Cheng ◽  
Jialie Shen ◽  
Lei Zhu ◽  
Mohan Kankanhalli ◽  
Liqiang Nie
Keyword(s):  
Recommender Systems ◽  
Feature Learning ◽  
Music Recommendation ◽  
Streaming Services ◽  
Rich Information ◽  
Music Recommender Systems ◽  
Public Datasets ◽  
Digital Footprints ◽  
Music Streaming ◽  
Music Play

Users leave digital footprints when interacting with various music streaming services. Music play sequence, which contains rich information about personal music preference and song similarity, has been largely ignored in previous music recommender systems. In this paper, we explore the effects of music play sequence on developing effective personalized music recommender systems. Towards the goal, we propose to use word embedding techniques in music play sequences to estimate the similarity between songs. The learned similarity is then embedded into matrix factorization to boost the latent feature learning and discovery. Furthermore, the proposed method only considers the k-nearest songs (e.g., k = 5) in the learning process and thus avoids the increase of time complexity. Experimental results on two public datasets demonstrate that our methods could significantly improve the performance of both rating prediction and top-n recommendation tasks.

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