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A hybrid recommender system for improving automatic playlist continuation

Author
Gatzioura, A.; Vinagre, J.; Jorge, A.; Sànchez-Marrè, M.
Type of activity
Journal article
Journal
IEEE transactions on knowledge and data engineering
Date of publication
2021-05-01
Volume
33
Number
5
First page
1819
Last page
1830
DOI
10.1109/TKDE.2019.2952099
Project funding
Intelligent Data sciencE and Artificial Intellidence
Repository
http://hdl.handle.net/2117/188974 Open in new window
URL
https://ieeexplore.ieee.org/document/8894369 Open in new window
Abstract
Although widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, userpreferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybri...
Citation
Gatzioura, A. [et al.]. A hybrid recommender system for improving automatic playlist continuation. "IEEE transactions on knowledge and data engineering", 1 Maig 2021, vol. 33, núm. 5, p. 1819-1830.
Keywords
Automatic playlist continuation, Beyond accuracy dimensions, Case-based reasoning, Hybrid recommender system, Latent dirichlet allocation, Music recommender systems
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants

  • Gatzioura, Anna  (author)
  • Vinagre, João  (author)
  • Jorge, Alípio Mário  (author)
  • Sànchez Marrè, Miquel  (author)

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