文献类型：会议论文

英文题名：On Member Search Engine Selection Using Artificial Neural Network in Meta Search Engine

作者：Liu, Denghong[1];Xu, Xian[1];Long, Yu[2]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai, Peoples R China

会议论文集：16th IEEE/ACIS International Conference on Computer and Information Science (ICIS)

会议日期：MAY 24-26, 2017

会议地点：Wuhan Univ, Wuhan, PEOPLES R CHINA

主办单位：Wuhan Univ

语种：英文

外文关键词：Meta search engine; selection; weighted round robin; artificial neural network

摘要：Meta search engine is an effective tool for searching information online. In comparison with independent search engine like Google, Bing, and etc., meta search engine has a wider coverage and can meet the requirement of information retrieval in a better manner. In particular, when a query is received from the user, the meta search engine sends it to some proper candidate member engines, collects results from them, and then replies to the user. An important issue here is how to better select the underlying member search engines. In this paper, we focus on the engine selection in meta search engine. We propose a selection design based on the combination of weighted round robin algorithm and artificial neural network. The experimental results show that our design can indeed improve the relevancy between the query and member search engine, and thus the effectivity of member selection.