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Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review

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Advances in Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1463))

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Abstract

The increasing popularity of social networks indicates that the vast amounts of data contained within them could be useful in various implementations, including recommendation systems. Interests and research publications on deep learning-based recommendation systems have largely increased. This study aimed to identify, summarize, and assess studies related to the application of deep learning-based recommendation systems on social media platforms to provide a systematic review of recent studies and provide a way for further research to improve the development of deep learning-based recommendation systems in social environments. A total of 32 papers were selected from previous studies in five of the major digital libraries, including Springer, IEEE, ScienceDirect, ACM, Scopus, and Web of Science, published between 2016 and 2020. Results revealed that even though RS has received high coverage in recent years, several obstacles and opportunities will shape the future of RS for researchers. In addition, social recommendation systems achieving high accuracy can be built by using a combination of techniques that incorporate a range of features in SRS. Therefore, the adoption of deep learning techniques in developing social recommendation systems is undiscovered.

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Acknowledgements

This work was supported/funded by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1). The authors sincerely thank Universiti Teknologi Malaysia under Research University Grant Vot-20H04 and Malaysia Research University Network Vot 4L876, for the completion of the research. The work is also partially supported by the SPEV project (ID: 2102–2021), Faculty of Informatics and Management, University of Hradec Kralove. We are also grateful for the support of Ph.D. students Michal Dobrovolny and Sebastien Mambou in consultations regarding application aspects from Hradec Kralove University, Czech Republic.

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Authors and Affiliations

  1. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Malaysia

    Muhammad Alrashidi, Ali Selamat & Roliana Ibrahim

  2. Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia (UTM), Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Ali Selamat

  3. Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Ali Selamat

  4. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Kralove, Czech Republic

    Ali Selamat & Ondrej Krejcar

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  1. Muhammad Alrashidi
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  2. Ali Selamat
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  4. Ondrej Krejcar
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Corresponding author

Correspondence to Ali Selamat .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  2. VU Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. University of the West of England, Bristol, UK

    Elias Pimenidis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

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Alrashidi, M., Selamat, A., Ibrahim, R., Krejcar, O. (2021). Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-88113-9_2

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