Personalised network activity feeds: finding needles in the haystacks

Shlomo Berkovsky, Jill Freyne

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationMining, modeling, and recommending 'Things' in Social Media
Subtitle of host publication4th International Workshops MUSE 2013, Prague, and MSM 2013, Paris. Revised Selected Papers
EditorsMartin Atzmueller, Alvin Chin, Christoph Scholz, Christoph Trattner
Place of PublicationCham
PublisherSpringer, Springer Nature
Chapter2
Pages21-34
Number of pages14
ISBN (Electronic)9783319147239
ISBN (Print)9783319147222
DOIs
Publication statusPublished - 2013
Externally publishedYes

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume8940
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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