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

  • Authors:

  • Ying Zhang;Bernard J. Jansen;Amanda Spink

  • Affiliations:

  • The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, College of Engineering, The Pennsylvania State University, University Park, PA 16802;329F Information Sciences and Technology Building, College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802;Faculty of Information Technology, Queensland University of Technology Gardens Point Campus, 2 George St, GPO Box 2434, Brisbane QLD 4001 Australia

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2009

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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. © 2009 Wiley Periodicals, Inc.