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An intelligent recommendation system in e-commerce using ensemble learning

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Abstract

In today’s world, recommendation systems play a vital role in customer analysis on social media, online businesses, e-commerce, etc. There are multiple sources of information on the Internet giving people a large set of suggestions and advice. This may create confusion for the accurate decision to the user and he/she may get lost in the competitive and growing market. A recommendation system is an essential part of e-commerce to supply the filtered relevant information asked by the customer. The major pitfalls of the existing recommendation system are flooding unnecessary recommendations and unpredictability about new products. Most of the recommendation systems rely on the purchase history of the customer and give suggestions for new products. Along with the history of the user’s purchase, it is crucial to analyze various other activities such as browsing history, wish lists, reviews, ratings, and previously ordered items. An intelligent recommendation system using ensemble learning is presented in this paper to reduce duplicate and irrelevant recommendations for the customer. The experimental results indicate that there has been a significant improvement in the precision and recall of the recommendation system in comparison with the other conventional techniques.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Cyber Systems Engineering, WMG, University of Warwick, Coventry, CV74AL, UK

    Achyut Shankar

  2. School of Computer Science Engineering, Lovely Professional University, 144411, Punjab, Phagwara, India

    Achyut Shankar

  3. Department of Computer Science and Engineering, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, 639 113, India

    Pandiaraja Perumal

  4. School of Computer Science and Engineering, Vellore Institute of Technology , Vellore, Tamil Nadu , 632014, India

    Murali Subramanian

  5. Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Chennai, Tamil Nadu, 603 203, India

    Naresh Ramu & Deepa Natesan

  6. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

    Vaishali R. Kulkarni

  7. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

    Thompson Stephan

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  1. Achyut Shankar
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Correspondence to Pandiaraja Perumal.

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Shankar, A., Perumal, P., Subramanian, M. et al. An intelligent recommendation system in e-commerce using ensemble learning. Multimed Tools Appl 83, 48521–48537 (2024). https://doi.org/10.1007/s11042-023-17415-1

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