Identification of factors predicting clickthrough in Web searching using neural network analysis

Jansen, Bernard, Spink, Amanda, & Zhang, Ying (2008) Identification of factors predicting clickthrough in Web searching using neural network analysis. Journal of the American Society for Information Science and Technology, 60(3), pp. 557-570.

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In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing

Impact and interest:

5 citations in Scopus
3 citations in Web of Science®
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ID Code: 30667
Item Type: Journal Article
Refereed: Yes
Keywords: Online Searching, Neural Networks, Retrieval Effectiveness, Search Engine Optimization, Link Analysis
DOI: 10.1002/asi.20993
ISSN: 1532-2882
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > OTHER INFORMATION AND COMPUTING SCIENCES (089900)
Copyright Owner: © 2008 ASIS&T
Deposited On: 12 Feb 2010 12:44
Last Modified: 29 Feb 2012 13:52

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