QUT ePrints

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.

View at publisher

Abstract

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:

3 citations in Scopus
Search Google Scholar™
1 citations in Web of Science®

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 30667
Item Type: Journal Article
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 22:44
Last Modified: 29 Feb 2012 23:52

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page