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Friend Recommendation in a Social Bookmarking System: Design and Architecture Guidelines

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Intelligent Systems in Science and Information 2014 (SAI 2014)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 591))

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Abstract

Social media systems allow users to share resources with the people connected to them. In order to handle the exponential growth of the content in these systems and of the amount of users that populate them, recommender systems have been introduced. As social media systems with different purposes arose, also different types of social recommender systems were developed in order to filter the specific information that each domain handles. A form of social media, known as social bookmarking system, allows to share bookmarks in a social network. A user adds as a friend or follows another user and receives updates on the bookmarks added by that user. In this paper, we present an analysis of the state-of-the-art on user recommendation in social environments and of the structure of a social bookmarking system, in order to derive design guidelines and an architecture of a friend recommender system in the social bookmarking domain. This study can be useful for future research, by highlighting the aspects that characterize this domain and the features that this type of recommender system has to offer.

This work is partially funded by Regione Sardegna under project SocialGlue, through PIA—Pacchetti Integrati di Agevolazione “Industria Artigianato e Servizi” (annualità 2010).

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Notes

  1. 1.

    http://www.delicious.com.

  2. 2.

    http://www.citeulike.org/.

  3. 3.

    http://www.flickr.com/.

  4. 4.

    http://techcrunch.com/2014/02/10/flickr-at-10-1m-photos-shared-per-day-170-increase-since-making-1tb-free/.

  5. 5.

    Given that traditional techniques to manually categorize data cannot be applied in social environments [4] and that clustering techniques represent a good form to extract information for recommendation purposes [3], the resources could be clustered based on the tags used to classify them, in order to extract some meta-information about a group of resources related to a specific topic.

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Authors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università di Cagliari, Via Ospedale 72, 09124, Cagliari, Italy

    Matteo Manca, Ludovico Boratto & Salvatore Carta

Authors
  1. Matteo Manca
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  2. Ludovico Boratto
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  3. Salvatore Carta
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Correspondence to Ludovico Boratto .

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Editors and Affiliations

  1. Graduate School of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, London, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, London, United Kingdom

    Rahul Bhatia

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Manca, M., Boratto, L., Carta, S. (2015). Friend Recommendation in a Social Bookmarking System: Design and Architecture Guidelines. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems in Science and Information 2014. SAI 2014. Studies in Computational Intelligence, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-14654-6_14

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  • DOI: https://doi.org/10.1007/978-3-319-14654-6_14

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