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A Survey on the Scalability of Recommender Systems for Social Networks

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Social Networks Science: Design, Implementation, Security, and Challenges

Abstract

It is typical among online social networks’ users to share their status, activity and other information with fellow users, to interact on the information shared by others and to express their trust or interest for each other. The result is a rich information repository which can be used to improve the user experience and increase their engagement if handled properly. In order to create a personalized user experience in social networks, we need data management solutions that scale well on the huge amounts of information generated on a daily basis. The social information of an online social network can be useful both for improving content personalization but also for allowing existing algorithms to scale to huge datasets. All current real-world large-scale recommender systems have invested on scalable distributed database systems for data storage and parallel and distributed algorithms for finding recommendations. This chapter, focuses on collaborative filtering algorithms for recommender systems, briefly explains how they work and what their limitations are.

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Author information

Authors and Affiliations

  1. Department of Informatics and Telematics, Harokopio University of Athens, Omirou 9, Athens, Greece

    C. Sardianos & I. Varlamis

  2. Department of Computer Engineering & Informatics, University of Patras, Rio, Patras, 26500, Greece

    N. Tsirakis

Authors
  1. C. Sardianos
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  2. N. Tsirakis
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  3. I. Varlamis
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Correspondence to C. Sardianos .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  2. Department of Information Systems, ISCAP, Porto Polytechnic, Porto, Portugal

    Rosalina Babo

  3. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  4. Department of Computer Science and Engineering, Ambedkar Institute of Advanced Communication Technologies and Research, New Delhi, India

    Vishal Bhatnagar

  5. Research Lab Sciences and Technologies of Image and Telecommunication, Sfax University, Sfax, Tunisia

    Med Salim Bouhlel

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Sardianos, C., Tsirakis, N., Varlamis, I. (2018). A Survey on the Scalability of Recommender Systems for Social Networks. In: Dey, N., Babo, R., Ashour, A., Bhatnagar, V., Bouhlel, M. (eds) Social Networks Science: Design, Implementation, Security, and Challenges . Springer, Cham. https://doi.org/10.1007/978-3-319-90059-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-90059-9_5

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