.png){kind=icon}
.png){kind=icon}
Return
Machine learning for user modeling
.png){kind=icon}
OA
AI
DOI：10.1023/A:1011117102175.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
user modeling
machine learning
concept drift
computational complexity
World Wide Web
information agents
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
U
IF:
3.5
Papers:
532
Citations:
1.7K
Organization
No organization information available