The Random Neural Network and Web Search: Survey Paper

被引:0
作者
Serrano, Will [1 ]
机构
[1] Imperial Coll London, Intelligent Syst & Networks Grp, London, England
来源
INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 1 | 2019年 / 868卷
关键词
Neural networks; Web search; Ranking algorithms; Deep learning; GOOGLE SCHOLAR; MULTIPLE CLASSES; RANKING ALGORITHM; VIDEO QUALITY; H-INDEX; INFORMATION; PERFORMANCE; INTERNET; SYSTEMS; SCOPUS;
D O I
10.1007/978-3-030-01054-6_51
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
E-commerce customers and general Web users should not believe that the products suggested by Recommender systems or results displayed by Web search engines are either complete or relevant to their search aspirations. The economic priority of Web related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers; furthermore, Web search engines and recommender systems revenue is obtained from advertisements and pay-per-click. This survey paper presents a review of Web Search Engines, Ranking Algorithms, Citation Analysis and Recommender Systems. In addition, Neural networks and Deep Learning are also analyzed including their use in learning relevance and ranking. Finally, this survey paper also introduces the Random Neural Network with its practical applications.
引用
收藏
页码:700 / 737
页数:38
相关论文
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