A formal model for user preference

被引：0
|
作者：
Jung, SY [1 ]
Hong, JH [1 ]
Kim, TS [1 ]
机构：
[1] LG Elect Inst Technol, Machine Intelligence Grp, Seoul 137140, South Korea
来源：
2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2002年
关键词：
user preference; recommendation; personalization; data sparseness; pareto distribution; random occurrence probability; dynamic weighting model;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Personalization and recommendation systems require formalized model for user preference. This paper presents the formal model of preference including positive preference and negative preference. For rare events, we apply the probability of random occurrence in order to reduce noise effects caused by data sparseness. Pareto distribution is adopted for the random occurrence probability. We also present the method for combining information of joint feature variables in different sizes by dynamic weighting using random occurrence probability.
引用
收藏
页码：235 / 242
页数：8
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