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

Ying Zhang, Bernard J. Jansen, Amanda Spink

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)557-570
Number of pages14
JournalJournal of the American Society for Information Science and Technology
Issue number3
StatePublished - Mar 2009

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence


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