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Sunday, 10 August 2014

Recommending social media content to community owners

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

2014, Pages 243-252
37th International ACM SIGIR Conference on Research and Development
in Information Retrieval, SIGIR 2014; Gold Coast, QLD; Australia; 6
July 2014 through 11 July 2014; Code 106461

Recommending social media content to community owners  (Conference Paper)


IBM Research, Haifa, Israel




Yahoo Labs, Haifa, Israel




Department of Computer Science, Technion, Haifa, Israel





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.
Copyright © 2014 ACM.

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Author keywords

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


Scopus - Document details

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