Normal Distribution Re-Weighting for Personalized Web Search

被引：0
|
作者：
Liu, Hanze [1 ]
Hoeber, Orland [1 ]
机构：
[1] Mem Univ Newfoundland, Dept Comp Sci, St John, NF A1C 5S7, Canada
来源：
ADVANCES IN ARTIFICIAL INTELLIGENCE | 2011年 / 6657卷
关键词：
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：
Personalized Web search systems have been developed to tailor Web search to users' needs based on their interests and preferences. A novel Normal Distribution Re-Weighting (NDRW) approach is proposed in this paper, which identifies and re-weights significant terms in vector-based personalization models in order to improve the personalization process. Machine learning approaches will be used to train the algorithm and discover optimal settings for the NDRW parameters. Correlating these parameters to features of the personalization model will allow this re-weighting process to become automatic.
引用
收藏
页码：281 / 284
页数：4
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