Predictive statistical models for user modeling

被引：146

作者：

Zukerman, I ^{[1
]}

Albrecht, DW ^{[1
]}

机构：

[1] Monash Univ, Sch Comp Sci & Software Engn, Clayton, Vic 3800, Australia

来源：

USER MODELING AND USER-ADAPTED INTERACTION | 2001年 / 11卷 / 1-2期

关键词：

content-based learning; collaborative learning; linear models; TFIDF-based models; Markov models; Neural networks; classifications; rule induction; Bayesian networks;

D O I：

10.1023/A:1011175525451

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of statistical models. Predictive statistical models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences. In this paper, we motivate the development of these models in the context of the user modeling enterprise. We then review the two main approaches to predictive statistical modeling, content-based and collaborative, and discuss the main techniques used to develop predictive statistical models. We also consider the evaluation requirements of these models in the user modeling context, and propose topics for future research.

引用

页码：5 / 18

页数：14

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