.png){kind=icon}
返回
An incremental model on search engine query recommendation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2016.09.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Search engine query recommendation based on mining query logs has been considered as an important and useful method of facilitating users to retrieve information. However, the log data evolves quickly. Existing query recommendation approaches have to rebuild the models when new log data arrive. In this paper, we extend the query ranking model (QRM) proposed in our previous work (Wang et al., 2015) 111 to an adaptive model in which new coming log data is incrementally added, so that the recommendation model is kept up-to-date. The experimental results have demonstrated that the proposed incremental query ranking model (IQRM) is able to recommend queries more efficiently than re-building QRM on evolving log data without losing accuracy. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Query recommendation
Query log analysis
Convex optimization
Incremental update method
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
Three-dimensional grayscale-free topology optimization using a level-set based r-refinement method基于水平集r-细化方法的三维无灰度拓扑优化
.png){kind=icon}
PREAI
分享收藏分享收藏分享收藏分享收藏
分享收藏
查看更多内容