research-article

Recommending social media content to community owners

Authors:

Maya BarneaAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 243 - 252

https://doi.org/10.1145/2600428.2609596

Published: 03 July 2014 Publication History

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.

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Benouaret KTan K(2023)Probabilistic Majority Rule-Based Group Recommendation2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00118(1489-1501)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00118
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Index Terms

Recommending social media content to community owners
1. Information systems
  1. Information retrieval

Recommendations

Social media recommendation based on people and tags
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cover image ACM Conferences

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 03 July 2014

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SIGIR '14

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SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Benouaret KTan K(2023)Probabilistic Majority Rule-Based Group Recommendation2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00118(1489-1501)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00118
Gligoric KCzestochowska JAnderson AWest R(2022)Anticipated versus Actual Effects of Platform Design Change: A Case Study of Twitter's Character LimitProceedings of the ACM on Human-Computer Interaction10.1145/35556596:CSCW2(1-29)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555659
Phelan CHeyer JPfafman RKerrigan CTzilos Wernette GDombrowski LMiller APater J(2022)The Work of Digital Social Re-entry in Substance Use Disorder RecoveryProceedings of the ACM on Human-Computer Interaction10.1145/35556586:CSCW2(1-33)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555658
Kotut LHorning MMcCrickard D(2022)The Long Way Home: News Values in Stories Told by Appalachian Trail Thru-Hikers on Social MediaProceedings of the ACM on Human-Computer Interaction10.1145/35556026:CSCW2(1-21)Online publication date: 11-Nov-2022
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Lee AMieczkowski HEllison NHancock J(2022)The Algorithmic Crystal: Conceptualizing the Self through Algorithmic Personalization on TikTokProceedings of the ACM on Human-Computer Interaction10.1145/35556016:CSCW2(1-22)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555601
Gao GZheng JChoe EYamashita N(2022)Taking a Language Detour: How International Migrants Speaking a Minority Language Seek COVID-Related Information in Their Host CountriesProceedings of the ACM on Human-Computer Interaction10.1145/35556006:CSCW2(1-32)Online publication date: 11-Nov-2022
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Chalhoub GFlechais I(2022)Data Protection at a Discount: Investigating the UX of Data Protection from User, Designer, and Business Leader PerspectivesProceedings of the ACM on Human-Computer Interaction10.1145/35555376:CSCW2(1-36)Online publication date: 11-Nov-2022
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Alvarado OHtun NJin YVerbert K(2022)A Systematic Review of Interaction Design Strategies for Group Recommendation SystemsProceedings of the ACM on Human-Computer Interaction10.1145/35551616:CSCW2(1-51)Online publication date: 11-Nov-2022
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Tian ZLiu YSun JJiang YZhu M(2021)Exploiting Group Information for Personalized Recommendation with Graph Neural NetworksACM Transactions on Information Systems10.1145/346476440:2(1-23)Online publication date: 27-Sep-2021
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Raheem Hal-assadi T(2021)Recommender Systems for E-commerce in online video advertising: Survey2021 International Conference on Advanced Computer Applications (ACA)10.1109/ACA52198.2021.9626785(68-73)Online publication date: 25-Jul-2021
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