文献类型：期刊文献

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] Department of Computer Science and Engineering, East China University of Science and Technology, 200237, China; [2] Department of Computer Science and Engineering, Shanghai Jiao Tong University, 200240, China

年份：2017

起止页码：865

外文期刊名：Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017

收录：EI(收录号：20174104250764)

语种：英文

外文关键词：Search engines - Routers

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. ? 2017 IEEE.