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

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  • Ying Zhang
  • Bernard J. Jansen
  • Amanda Spink

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.

Suggested Citation

  • Ying Zhang & Bernard J. Jansen & Amanda Spink, 2009. "Identification of factors predicting clickthrough in Web searching using neural network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 557-570, March.
    • Handle: RePEc:bla:jamist:v:60:y:2009:i:3:p:557-570
    DOI: 10.1002/asi.20993
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    Cited by:

    1. Jabłońska-Sabuka, Matylda & Sitarz, Robert & Kraslawski, Andrzej, 2014. "Forecasting research trends using population dynamics model with Burgers’ type interaction," Journal of Informetrics, Elsevier, vol. 8(1), pages 111-122.
    2. Andrés Martínez & Alfonso Salafranca & Ana E. Sipols & Clara Simon Blas & Daniel Hengel, 2024. "Distributed lags using elastic-net regularization for market response models: focus on predictive and explanatory capacity," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 417-435, June.

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