.png){kind=icon}
返回
Group-enhanced ranking
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2014.03.079.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An essential issue in document retrieval is ranking, which is used to rank documents by their relevancies to a given query. This paper presents a novel machine learning framework for ranking based on document groups. Multiple level labels represent the relevance of documents. The values of labels are used to quantify the relevance of the documents. According to a given query in the training set, the documents are divided into several groups based upon their relevance labels. The group with higher relevance labels is always ranked upon the ones with lower relevance labels. Further a preference strategy is introduced in the loss functions, which are sensitive to the group with higher relevance labels to enhance the group ranking method. Experimental results illustrate that the proposed approach is very effective, with a 14 percent improvement on TD2003 dataset evaluated by MAP. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Information retrieval
Learning to rank
Groups
Loss functions
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
N-methyl-d-aspartate-induced nitric oxide release: An in vivo microdialysis study.png)
Neuroscience
NeuroscienceIF0
PREAI
分享收藏Kinome-Wide Screening with Small Interfering RNA Identified Polo-like Kinase 1 as a Key Regulator of Proliferation in Oral Cancer CellsCancers
OAAI
CancersIF0
OAAI分享收藏分享收藏
查看更多内容