Ranking Algorithm Based on Relational Topic Model

被引：0
|
作者：
Ding, Yuxin [1 ]
Yan, Shengli [1 ]
Xiao, Yang [1 ]
Tao, Tingting [1 ]
机构：
[1] Harbin Inst Technol, Shenzhen Grad Sch, Dept Comp Sci & Technol, Shenzhen, Peoples R China
来源：
2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2015年
关键词：
Ranking; Machine Learning; Topic Relation Model; Information Retrieval;
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：
In this paper a supervised topic model is proposed for rank learning. The original supervised topic model can only learn from positive samples. For rank learning problem, training data have different ranking labels. To solve this issue, we extend the supervised topic model and make it learn from training data with different ranking labels. The experiments show that the proposed topic models can find the hidden relationships among words, and have higher ranking accuracy than word based models. In addition, the supervised topic models have higher ranking accuracy than the unsupervised topic models.
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收藏
页数：8
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