Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Samantha Hansen"'
Autor:
Nancy Viau
Big rock collector. Big mouth. BIG problem! Ten-year-old Samantha Hansen loves science, especially rocks and minerals. Her family is planning a dream-come-true trip to the Grand Canyon where Sam will finally see the “biggest chunk of sedimentary ro
Autor:
Nancy Viau
Ten-year-old Samantha Hansen loves science! In the beginning of fourth grade, she never let a moment go by without talking about rocks. Now she's back with a new obsession: insects! Upon learning that the local apiary is for sale, she goes into actio
Autor:
Gray, Ann M. G.1
Publikováno v:
Library Media Connection. Mar/Apr2009, Vol. 27 Issue 5, p71-71. 1/8p.
Publikováno v:
Kirkus Reviews. 8/15/2008, Vol. 76 Issue 16, p174-174. 1p.
Large-scale user modeling with recurrent neural networks for music discovery on multiple time scales
Autor:
Samantha Hansen, Bart Dhoedt, Romain Yon, Rohan Agrawal, Thomas Demeester, Ching-Wei Chen, Esh Kumar, Cedric De Boom
Publikováno v:
Multimedia Tools and Applications. 77:15385-15407
The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23304b8b547844c4cff856c71ad34c02
https://doi.org/10.1007/s11042-017-5121-z
https://doi.org/10.1007/s11042-017-5121-z
Autor:
Praveen Chandar, Alois Gruson, Christophe Charbuillet, Samantha Hansen, Ben Carterette, Damien Tardieu, James McInerney
Publikováno v:
WSDM
Evaluating algorithmic recommendations is an important, but difficult, problem. Evaluations conducted offline using data collected from user interactions with an online system often suffer from biases arising from the user interface or the recommenda
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b46c110c7b9de14d8a2a0a4dcc47def
https://doi.org/10.1145/3289600.3291027
https://doi.org/10.1145/3289600.3291027
Autor:
James McInerney, Hugues Bouchard, Alois Gruson, Karl Higley, Benjamin Lacker, Samantha Hansen, Rishabh Mehrotra
Publikováno v:
RecSys
The multi-armed bandit is an important framework for balancing exploration with exploitation in recommendation. Exploitation recommends content (e.g., products, movies, music playlists) with the highest predicted user engagement and has traditionally
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d8cd3e2db962af49a2a1dd61da271a6
https://doi.org/10.1145/3240323.3240354
https://doi.org/10.1145/3240323.3240354