Machine learning for user modeling
Data(s) |
01/03/2001
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Resumo |
At first blush, user modeling appears to be a prime candidate for straightforward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labeled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer Netherlands |
Relação |
http://dro.deakin.edu.au/eserv/DU:30001054/webb-machinelearningfor-2001.pdf http://dx.doi.org/10.1023/A:1011117102175 |
Direitos |
2001, Kluwer Academic Publishers |
Palavras-Chave | #User modeling #Machine learning #Concept drift #Computational complexity #World wide web #Information agents |
Tipo |
Journal Article |