Recommending social media content to community owners

Inbal Ronen, Ido Guy, Elad Kravi, Maya Barnea

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

34 Scopus citations

Abstract

Online communities within the enterprise offer their leaders an easy and accessible way to attract, engage, and influence others. Our research studies the recommendation of social media content to leaders (owners) of online communities within the enterprise. We developed a system that suggests to owners new content from outside the community, which might interest the community members. As online communities are taking a central role in the pervasion of social media to the enterprise, sharing such recommendations can help owners create a more lively and engaging community. We compared seven different methods for generating recommendations, including content-based, member-based, and hybridization of the two. For member-based recommendations, we experimented with three groups: owners, active members, and regular members. Our evaluation is based on a survey in which 851 community owners rated a total of 8,218 recommended content items. We analyzed the quality of the different recommendation methods and examined the effect of different community characteristics, such as type and size.

Original languageEnglish
Title of host publicationSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages243-252
Number of pages10
ISBN (Print)9781450322591
DOIs
StatePublished - 1 Jan 2014
Externally publishedYes
Event37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 - Gold Coast, QLD, Australia
Duration: 6 Jul 201411 Jul 2014

Publication series

NameSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014
Country/TerritoryAustralia
CityGold Coast, QLD
Period6/07/1411/07/14

Keywords

  • Enterprise
  • Group recommendation
  • Online communities
  • Recommender systems
  • Social media

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