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Personalised Network Activity Feeds: Finding Needles in the Haystacks

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Book cover Mining, Modeling, and Recommending 'Things' in Social Media (MUSE 2013, MSM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8940))

Abstract

Social networks have evolved over the last decade into an omni-popular phenomenon that revolutionised both the online and offline interactions between people. The volume of user generated content for discovery on social networks is overwhelming and ever growing, and while time spend on social networking sites has increased, the flood of incoming information still greatly exceeds the capacity of information that any one user can deal with. Personalisation of social network activity news feeds is proposed as the solution that highlights and promotes items of a particular interest and relevance, in order to prioritise attention and maximise discovery for the user. In this chapter, we survey and examine the various research approaches for the personalisation of social network news feeds and identify the synergies and challenges faced by research in this space.

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Notes

  1. 1.

    Notwithstanding, the under-water part of this iceberg includes applied research done by large-scale social networks, such as Facebook, LinkedIn, and Twitter. They put much effort into feed filtering and develop proprietary solutions, but these are most often not disclosed due to the commercial sensitivity and competitiveness.

  2. 2.

    http://www.summify.com.

  3. 3.

    http://www.twingly.com/screensaver.

  4. 4.

    http://sourceforge.net/projects/fidgtvisual/.

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Authors and Affiliations

  1. CSIRO, Epping, Australia

    Shlomo Berkovsky & Jill Freyne

Authors
  1. Shlomo Berkovsky
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  2. Jill Freyne
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Corresponding author

Correspondence to Shlomo Berkovsky .

Editor information

Editors and Affiliations

  1. University of Kassel, Kassel, Germany

    Martin Atzmueller

  2. Microsoft, Beijing, China

    Alvin Chin

  3. University of Kassel, Kassel, Germany

    Christoph Scholz

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Christoph Trattner

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Berkovsky, S., Freyne, J. (2015). Personalised Network Activity Feeds: Finding Needles in the Haystacks. In: Atzmueller, M., Chin, A., Scholz, C., Trattner, C. (eds) Mining, Modeling, and Recommending 'Things' in Social Media. MUSE MSM 2013 2013. Lecture Notes in Computer Science(), vol 8940. Springer, Cham. https://doi.org/10.1007/978-3-319-14723-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-14723-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14722-2

  • Online ISBN: 978-3-319-14723-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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