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Exclusive Item Recommendation to the Online Shopping Customers Based on Category Using Clickstream and UID Matrix

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Computer Networks and Inventive Communication Technologies

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 141))

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Abstract

Online shopping becomes indispensable among the people worldwide. Clickstream, collaborative filtering and machine learning algorithms play a considerable role to analyze the browsing behavior and predict the next click of the customers. In this research, k-nearest neighbor is applied to classify the customers into three groups: Regular, Special and Exceptional. User-Item-Detail matrix is constructed to identify the similarity among the online customers. Exclusive recommendation is provided to the customers based on user classification. The accuracy of the research is evaluated with the parameters precision, recall, and f-scores.

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Authors and Affiliations

  1. Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode, Tamilnadu, 638401, India

    R. Suguna & P. Sathishkumar

  2. Computer Technology-PG, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India

    S. Deepa

Authors
  1. R. Suguna
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  2. P. Sathishkumar
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  3. S. Deepa
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Corresponding author

Correspondence to R. Suguna .

Editor information

Editors and Affiliations

  1. Department of CSE, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Department of Telecommunication Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Pavel Lafata

  3. Gerald Schwartz School of Business, St. Francis Xavier University, Antigonish, NS, Canada

    Ram Palanisamy

  4. Department of Computer Science, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

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Suguna, R., Sathishkumar, P., Deepa, S. (2023). Exclusive Item Recommendation to the Online Shopping Customers Based on Category Using Clickstream and UID Matrix. In: Smys, S., Lafata, P., Palanisamy, R., Kamel, K.A. (eds) Computer Networks and Inventive Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-19-3035-5_14

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  • DOI: https://doi.org/10.1007/978-981-19-3035-5_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-3034-8

  • Online ISBN: 978-981-19-3035-5

  • eBook Packages: EngineeringEngineering (R0)

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