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Personalised Network Activity Feeds: Finding Needles in the Haystacks

Part of the Lecture Notes in Computer Science book series (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.

Keywords

  • Social Network
  • Ensemble Model
  • User Interest
  • Social Network User
  • Latent Factor Model

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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Correspondence to Shlomo Berkovsky .

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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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