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A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 451)

Abstract

In recent years, deep learning has gotten a lot of attention, notably in fields like Computer Vision and Natural Language Processing. With the growing amount of online information, recommender systems have shown to be an effective technique for coping with information overload. The purpose of this article is to provide a comprehensive overview of recent deep learning-based recommender systems. Furthermore, it provides an experimental assessment of prominent topics within the latest published papers in the field. Results showed that explainable AI and Graph Neural Networks are two of the most attractive topics in the field to this day, and that the adoption of deep learning methods is increasing over.

Keywords

  • Recommender systems
  • Deep learning
  • Research trend analysis

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Acknowledgments

This paper has been produced with the financial support of the Justice Programme of the European Union, 101046629 CREA2, JUST-2021-EJUSTICE, JUST2027 Programme. The contents of this report are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Commission.

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Correspondence to Mattia Fonisto .

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Amato, F., Di Cicco, F., Fonisto, M., Giacalone, M. (2022). A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_10

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