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Deep Learning-Based Recommender Systems—A Systematic Review and Future Perspective

  • Conference paper
  • First Online:
Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

  • 166 Accesses

Abstract

With the ever-increasing volume of online information, recommender systems (RS) have been an effective technique to overcome such information overload. Given its widespread implementation in a variety of web applications, the value of RS cannot be emphasized enough. RS has the potential to improve numerous issues identified with over-choice. Deep learning's influence is also widespread, with new evidence of its efficacy in information retrieval and RS research. In RS, the field of deep learning is emerging. This article presents a thorough evaluation of recent research findings on traditional RS and deep learning-based recommender systems (DLRS). This review covers articles published between 2018 and 2022 in four major research databases: Science Direct, IEEE Explore, Springer, and Wiley. First, we provide a complete overview, comparison, and summary of traditional RS. We then systematically analyze DLRS challenges and related solution approaches. Finally, under the discussion of open issues at DLRS, we highlight possible research directions in this area. In this review, we report quantitative data from previous studies to highlight comparisons based on metrics such as F1-score accuracy, recall, accuracy, and error functions.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering, Presidency University, Bengaluru, Karnataka, India

    S. Krishnamoorthi & Gopal K. Shyam

Authors
  1. S. Krishnamoorthi
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  2. Gopal K. Shyam
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Corresponding author

Correspondence to S. Krishnamoorthi .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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Krishnamoorthi, S., Shyam, G.K. (2023). Deep Learning-Based Recommender Systems—A Systematic Review and Future Perspective. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_33

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