检索结果-内蒙古大学图书馆

Learning to rank: new approach with the layered multi-population genetic programming on click-through features

genetic programming AND EVOLVABLE MACHINES 2016年第3期17卷 203-230页

作者： Keyhanipour, Amir Hosein Moshiri, Behzad Oroumchian, Farhad Rahgozar, Maseud Badie, Kambiz Univ Tehran Sch ECE Ctr Excellence Control & Intelligent Proc 5 Zolfaghary AlleyShadmehr StSattrKhan Ave Tehran Iran Univ Wollongong Dubai Fac Engn & Informat Sci Dubai U Arab Emirates Comp Soc Iran Tehran Iran

Users' click-through data is a valuable source of information about the performance of Web search engines, but it is included in few datasets for learning to rank. In this paper, inspired by the click-through data model, a novel approach is proposed for extracting the implicit user feedback from evidence embedded in benchmarking datasets. This process outputs a set of new features, named click-through features. Generated click-through features are used in a layered multi-population genetic programming framework to find the best possible ranking functions. The layered multi-population genetic programming framework is fast and provides more extensive search capability compared to the traditional genetic programming approaches. The performance of the proposed ranking generation framework is investigated both in the presence and in the absence of explicit click-through data in the utilized benchmark datasets. The experimental results show that click-through features can be efficiently extracted in both cases but that more effective ranking functions result when click-through features are generated from benchmark datasets with explicit click-through data. In either case, the most noticeable ranking improvements are achieved at the tops of the provided ranked lists of results, which are highly targeted by the Web users.

关键词： Learning to rank Click-through data layered multi-population genetic programming

来源：评论

学校读者我要写书评

暂无评论

Designing a Web Spam Classifier Based on Feature Fusion in the layered multi-population genetic programming Framework

Designing a Web Spam Classifier Based on Feature Fusion in t...

引用

16th International Conference on Information Fusion (FUSION)

作者： Keyhanipour, Amir Hosein Moshiri, Behzad Univ Tehran Coll Engn Sch Elect & Comp Engn Ctr Excellence Tehran Iran

ISBN: (纸本)9786058631113

Nowadays, Web spam pages are a critical challenge for Web retrieval systems which have drastic influence on the performance of such systems. Although these systems try to combat the impact of spam pages on their final results list, spammers increasingly use more sophisticated techniques to increase the number of views for their intended pages in order to have more commercial success. This paper employs the recently proposed layered multi-population genetic programming model for Web spam detection task as well application of correlation coefficient analysis for feature space reduction. Based on our tentative results, the designed classifier, which is based on a combination of easy to compute features, has a very reasonable performance in comparison with similar methods.

关键词： Web Spam Classifier layered multi-population genetic programming

来源：评论

学校读者我要写书评

暂无评论

Designing a Web Spam Classifier Based on Feature Fusion in the layered multi-population genetic programming Framework

Designing a Web Spam Classifier Based on Feature Fusion in t...

引用

International Conference on Information Fusion

作者： Amir Hosein Keyhanipour Behzad Moshiri Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran

ISBN: (纸本)9781479902842

关键词： Web Spam Classifier layered multi-population genetic programming

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：