Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Victor W. Chu"'
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 16:1-37
E-commerce platforms heavily rely on automatic personalized recommender systems, e.g., collaborative filtering models, to improve customer experience. Some hybrid models have been proposed recently to address the deficiency of existing models. Howeve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::181a73ff9e440789d2bde2296d0026eb
https://doi.org/10.1145/3470659
https://doi.org/10.1145/3470659
Autor:
Kok-Leong Ong, Jinyan Li, Kelvin K. L. Wong, Simon Fong, Yaoyang Wu, Raymond K. Wong, Victor W. Chu
Publikováno v:
The Journal of Supercomputing. 77:7549-7583
Process mining is becoming an indispensable method in workflow model reconstructions, offering insights into mission critical systems. The efficacy of process mining depends on whether the underlying data mining algorithms can accurately classify or
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1a210ddff51709bb95e66a784ecab79
https://doi.org/10.1007/s11227-020-03500-x
https://doi.org/10.1007/s11227-020-03500-x
Publikováno v:
International Journal of Data Science and Analytics. 12:15-29
© 2020, Springer Nature Switzerland AG. Sparsity and noisy labels occur inherently in real-world data. Previously, strong assumptions were made by domain experts to use their experience and expertise to select parameters for their models. Similar ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::240dda5f911c00f93b05a84b5b639299
https://doi.org/10.1007/s41060-020-00219-z
https://doi.org/10.1007/s41060-020-00219-z
Publikováno v:
IJCNN
Although matrix factorization and its variants have proved their effectiveness in learning user preferences, they violate the triangle inequality and may fail to capture the inner grained preference information. Thus, metric learning based models hav
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3245ae4b8f22b97e9ae33d4d1a115c5
https://doi.org/10.1109/ijcnn52387.2021.9533945
https://doi.org/10.1109/ijcnn52387.2021.9533945
Publikováno v:
Web Information Systems Engineering – WISE 2021 ISBN: 9783030908874
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::548040f1bbed8c3ed3729892780939e4
https://doi.org/10.1007/978-3-030-90888-1_15
https://doi.org/10.1007/978-3-030-90888-1_15
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783030757618
PAKDD (1)
PAKDD (1)
There is a growing interest in explainable machine learning methods. In our investigation, we have collected heterogeneous features from two series of YouTube video ads and seven series of Instagram picture ads to form our datasets. There are two mai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dbac07be316c2f354fc8b07c631608a8
https://doi.org/10.1007/978-3-030-75762-5_45
https://doi.org/10.1007/978-3-030-75762-5_45
Publikováno v:
ICDM (Workshops)
Latent variable models have been widely adopted by recommender systems due to the advancements of their learning scalability and performance. Recent research has focused on hybrid models. However, due to the sparsity of user and/or item data, most of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a497fe8cd869daee360a250105dc2dd
https://doi.org/10.1109/icdmw51313.2020.00040
https://doi.org/10.1109/icdmw51313.2020.00040
Publikováno v:
Information Systems Frontiers. 19:1283-1299
Devices embedded with position tracking facilities are now widely available, such as smartphones, smartwatches, vehicle location trackers, etc. However, data mining and advanced analytics are rarely bundled with these devices that limits their utilit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0679dc13b9a0eeeeddc61a2d9eabdb8f
https://doi.org/10.1007/s10796-017-9768-9
https://doi.org/10.1007/s10796-017-9768-9
While time-series analysis is commonly used in financial forecasting, a key source of market-sentiments is often omitted. Financial news is known to be making persuasive impact on the markets. Without considering this additional source of signals, on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e012cdbda923d35315026ad23e6949b
https://hdl.handle.net/10453/141368
https://hdl.handle.net/10453/141368
Publikováno v:
SCC
Matrix factorization is a popular method for building recommendation models. On e-commerce platforms, this method makes predictions of product ratings for goods which have not been rated. Similarly, in service computing, service rating platforms have
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4e43c7d086d3a825d9fbb7f86eb0e8b
https://doi.org/10.1109/scc.2019.00015
https://doi.org/10.1109/scc.2019.00015