TY - GEN
T1 - Recommending social media content to community owners
AU - Ronen, Inbal
AU - Guy, Ido
AU - Kravi, Elad
AU - Barnea, Maya
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
KW - Enterprise
KW - Group recommendation
KW - Online communities
KW - Recommender systems
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=84904541209&partnerID=8YFLogxK
U2 - 10.1145/2600428.2609596
DO - 10.1145/2600428.2609596
M3 - Conference contribution
AN - SCOPUS:84904541209
SN - 9781450322591
T3 - SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 243
EP - 252
BT - SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
T2 - 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014
Y2 - 6 July 2014 through 11 July 2014
ER -