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Interaction-Based Recommendations for Online Communities

Authors:

Payam Aghaei Pour,

Sanat Kumar BistaAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 15, Issue 2

Article No.: 6, Pages 1 - 21

https://doi.org/10.1145/2774974

Published: 24 June 2015 Publication History

Abstract

A key challenge in online communities is that of keeping a community active and alive. All online communities work hard to keep their members through various initiatives, such as personalisation and recommendation technologies. In online communities aimed at supporting behavioural change, that is, in domains such as diet, lifestyle, or the environment, the main reason for participation is not to connect with real-world friends for sharing and communicating, but to meet and gain support from like-minded people in an online environment. Introducing personalisation and recommendation features in these networks is challenging, as traditional approaches leverage the densely populated friendship relations found in typical social networks, and these are not present in these new community types. We address this challenge by looking beyond the articulated friendships of a community for evidence of relationships. In particular, we look at the interactions of members of an online community with other members and resources. In this article, we present a social behaviour model and apply it to two types of recommendation systems, a people recommender and a content recommender system. We evaluate our systems using the interaction logs of an online diet and lifestyle community in which 5,000 Australians participated in a 12-week programme. Our results show that our social behaviour-based recommendation algorithms outperform baselines, friendship-based, and link-prediction algorithms.

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Cited By

Jethava GRao U(2024)Exploring security and trust mechanisms in online social networks: An extensive reviewComputers & Security10.1016/j.cose.2024.103790140(103790)Online publication date: May-2024
https://doi.org/10.1016/j.cose.2024.103790
Frikha GLorca XPingaud HTaweel ABortolaso CBorgiel KLamine E(2024)Integrating Social Interaction Within Senselife FrameworkNavigating Unpredictability: Collaborative Networks in Non-linear Worlds10.1007/978-3-031-71743-7_2(19-33)Online publication date: 12-Sep-2024
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Wang YLee A(2023)Movie Account Recommendation on InstagramACM Transactions on Internet Technology10.1145/357985223:1(1-21)Online publication date: 13-Jan-2023
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Index Terms

Interaction-Based Recommendations for Online Communities
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 15, Issue 2

June 2015

89 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/2796692

Editor:
Munindar P. Singh
Department of Computer Science, North Carolina State University

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Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2015

Accepted: 01 May 2015

Revised: 01 January 2015

Received: 01 June 2014

Published in TOIT Volume 15, Issue 2

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Cited By

Jethava GRao U(2024)Exploring security and trust mechanisms in online social networks: An extensive reviewComputers & Security10.1016/j.cose.2024.103790140(103790)Online publication date: May-2024
https://doi.org/10.1016/j.cose.2024.103790
Frikha GLorca XPingaud HTaweel ABortolaso CBorgiel KLamine E(2024)Integrating Social Interaction Within Senselife FrameworkNavigating Unpredictability: Collaborative Networks in Non-linear Worlds10.1007/978-3-031-71743-7_2(19-33)Online publication date: 12-Sep-2024
https://doi.org/10.1007/978-3-031-71743-7_2
Wang YLee A(2023)Movie Account Recommendation on InstagramACM Transactions on Internet Technology10.1145/357985223:1(1-21)Online publication date: 13-Jan-2023
https://dl.acm.org/doi/10.1145/3579852
Masson ZParmentier G(2023)Drivers and mechanisms for online communities performance: A systematic literature reviewEuropean Management Journal10.1016/j.emj.2022.08.00541:4(590-606)Online publication date: Aug-2023
https://doi.org/10.1016/j.emj.2022.08.005
Bin Suhaim ABerri J(2022)Directional user similarity model for personalized recommendation in online social networksJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.10.01734:10(10205-10216)Online publication date: Nov-2022
https://doi.org/10.1016/j.jksuci.2022.10.017
Jethava GRao U(2022)A novel trust prediction approach for online social networks based on multifaceted feature similarityCluster Computing10.1007/s10586-022-03617-z25:6(3829-3843)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10586-022-03617-z
Choudhary CSingh IKumar M(2022)Community detection algorithms for recommendation systems: techniques and metricsComputing10.1007/s00607-022-01131-z105:2(417-453)Online publication date: 12-Nov-2022
https://dl.acm.org/doi/10.1007/s00607-022-01131-z
Gasparetti FSansonetti GMicarelli A(2020)Community detection in social recommender systems: a surveyApplied Intelligence10.1007/s10489-020-01962-3Online publication date: 25-Nov-2020
https://doi.org/10.1007/s10489-020-01962-3
Parmentier AP’ng JTan XCohen R(2020)Learning Reddit User Reputation Using Graphical Attention NetworksProceedings of the Future Technologies Conference (FTC) 2020, Volume 110.1007/978-3-030-63128-4_58(777-789)Online publication date: 31-Oct-2020
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Parmentier ACohen R(2019)Learning User Reputation on RedditIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352524(242-247)Online publication date: 14-Oct-2019
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