Social networking feeds: recommending items of interest

Jill Freyne, Shlomo Berkovsky, Elizabeth M. Daly, Werner Geyer

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

45 Citations (Scopus)


The success of social media has resulted in an information overload problem, where users are faced with hundreds of new contributions, edits and communications at every visit. A prime example of this in social networks is the news or activity feeds, where the actions (friending, commenting, photo sharing, etc) of friends on the network are presented to users in order to inform them of the network activity. In this work we endeavour to reduce the burden on individuals of identifying interesting updates in social network news feeds by automatically identifying and recommending relevant items to individuals where item relevance is based on the observed interactions of the individual with the social network. The results of our offline study show that combining short term interest models, exploiting previous viewing behavior of users, and long-term models, exploiting previous viewing of network actions, was the best predictor of feed item relevance.
Original languageEnglish
Title of host publicationProceedings of the fourth ACM conference on Recommender systems, RecSys '10
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Electronic)9781605589060
Publication statusPublished - 2010
Externally publishedYes
Event4th ACM Conference on Recommender Systems, RecSys 2010 - Barcelona, Spain
Duration: 26 Sept 201030 Sept 2010


Conference4th ACM Conference on Recommender Systems, RecSys 2010


  • personalization
  • Social Network
  • relevance
  • feeds


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