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https://brianmcfee.net/papers/hesas.pdf
HETEROGENEOUS EMBEDDING FOR SUBJECTIVE ARTIST SIMILARITY Brian McFee Computer Science and Engineering University of California, San Diego [email protected] Gert Lanckriet Electrical and Computer Engineering University of California, San Diego [email protected] ABSTRACT We describe an artist recommendation system which inte-
https://brianmcfee.net/posters/hesas.pdf
Heterogeneous Embedding for Subjective Artist Similarity Brian McFee and Gert Lanckriet University of California, San Diego Quantifying Consistency Similarity Prediction Total number of edges Retained edges after pruning direct inconsistencies Average size of maximal consistent subgraphs Number of edges included in all acyclic subgraphs 16385 ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.644.4762
Abstract. We describe an artist recommendation system which inte-grates several heterogeneous data sources to form a holistic similarity space. Using social, semantic, and acoustic fea-tures, we learn a low-dimensional feature transformation which is optimized to reproduce human-derived measure-ments of subjective similarity between artists.
https://www.researchgate.net/publication/220723109_Heterogeneous_Embedding_for_Subjective_Artist_Similarity
We describe an artist recommendation system which inte- grates several heterogeneous data sources to form a holistic similarity space. Using social, semantic, and acoustic fea- tures, we learn a...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.215.897
Abstract. We describe an artist recommendation system which integrates several heterogeneous data sources to form a holistic similarity space. Using social, semantic, and acoustic features, we learn a low-dimensional feature transformation which is optimized to reproduce human-derived measurements of subjective similarity between artists.
https://core.ac.uk/display/102676986
Heterogeneous embedding for subjective artist similarity . By Brian Mcfee and Gert Lanckriet. Abstract. We describe an artist recommendation system which inte-grates several heterogeneous data sources to form a holistic similarity space. Using social, semantic, and acoustic fea-tures, we learn a low-dimensional feature transformation which is ...Author: Brian Mcfee and Gert Lanckriet
https://link.springer.com/article/10.1007/s10791-013-9229-0
Heterogeneous embedding for subjective artist similarity. In Proceedings of the international symposium on music information retrieval ( ISMIR ). Mcfee, B., & Lanckriet, G. (2010).
https://dl.acm.org/doi/abs/10.1145/2187980.2188225
Apr 16, 2012 · Heterogeneous embedding for subjective artist similarity. In Proc. International Symposium on Music Information Retrieval (ISMIR), 2009. B. …
https://dl.acm.org/doi/10.1145/2542205.2542206
Dec 27, 2013 · Heterogeneous embedding for subjective artist similarity. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR'09). Mesnage, C. S., Rafiq, A., Dixon, S., and Brixtel, R. P. 2011. Music discovery with social networks.
https://www.ijcai.org/Proceedings/2018/0469.pdf
Network embedding aims to learn a vector representation of each node by mapping it into a low-dimensional vector space while preserving its neighborhood relationship. Because net-work embedding can capture the neighborhood similarity and community membership, it has been popularly used in rec-ommendations[Chenet al., 2015; Zhaoet al., 2016]. For
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