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Journal of New Music Research Volume 47, 2018 - Issue 1

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Original Articles

Mutual proximity graphs for improved reachability in music recommendation

Arthur FlexerAustrian Research Institute for Artificial Intelligence (OFAI), Austria.View further author information

Jeff StevensGeorge Mason University, Virginia, USA.View further author information

Pages 17-28 | Received 14 Feb 2017, Accepted 03 Jul 2017, Published online: 03 Aug 2017

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https://doi.org/10.1080/09298215.2017.1354891
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Citations (11)

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Articles from other publishers (11)

Daniel Obraczka & Erhard Rahm. (2022) Fast Hubness-Reduced Nearest Neighbor Search for Entity Alignment in Knowledge Graphs. SN Computer Science 3:6.
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Daliang Wang & Xiaowen Guo. (2022) Multidimensional Analysis of Music Education System Based on Multi-Intelligent Recommendation. Mobile Information Systems 2022, pages 1-8.
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Sudhanshu Kumar, Kanjar De & Partha Pratim Roy. (2020) Movie Recommendation System Using Sentiment Analysis From Microblogging Data. IEEE Transactions on Computational Social Systems 7:4, pages 915-923.
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Cornelia Metzig, Matthew Gould, Roshan Noronha, Roshani Abbey, Mark Sandler & Caroline Colijn. (2020) Classification of origin with feature selection and network construction for folk tunes. Pattern Recognition Letters 133, pages 356-364.
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Lingling Zhang, Minghui Zhao & Daozhen Zhao. (2020) Bipartite graph link prediction method with homogeneous nodes similarity for music recommendation. Multimedia Tools and Applications 79:19-20, pages 13197-13215.
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Xuan Chen. (2020) The application of neural network with convolution algorithm in Western music recommendation practice. Journal of Ambient Intelligence and Humanized Computing.
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Roman Feldbauer, Thomas Rattei & Arthur Flexer. (2020) scikit-hubness: Hubness Reduction and Approximate Neighbor Search. Journal of Open Source Software 5:45, pages 1957.
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Jingyi Hu & Ning Chen. (2019) Enhanced Feature Summarizing for Effective Cover Song Identification. IEEE/ACM Transactions on Audio, Speech, and Language Processing 27:12, pages 2113-2126.
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Roman Feldbauer & Arthur Flexer. (2018) A comprehensive empirical comparison of hubness reduction in high-dimensional spaces. Knowledge and Information Systems 59:1, pages 137-166.
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Yui Matsumoto, Ryosuke Harakawa, Takahiro Ogawa & Miki Haseyama. (2019) Music Video Recommendation Based on Link Prediction Considering Local and Global Structures of a Network. IEEE Access 7, pages 104155-104167.
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Arthur Flexer, Monika Dorfler, Jan Schluter & Thomas Grill. (2018) Hubness as a Case of Technical Algorithmic Bias in Music Recommendation. Hubness as a Case of Technical Algorithmic Bias in Music Recommendation.

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