tutorial

Social recommender systems

Authors:

Ido Guy,

David CarmelAuthors Info & Claims

WWW '11: Proceedings of the 20th international conference companion on World wide web

Pages 283 - 284

https://doi.org/10.1145/1963192.1963312

Published: 28 March 2011 Publication History

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Abstract

The goal of this tutorial is to expose participants to the current research on social recommender systems (i.e., recommender systems for the social web). Participants will become familiar with state-of-the-art recommendation methods, their classifications according to various criteria, common evaluation methodologies, and potential applications that can utilize social recommender systems. Additionally, open issues and challenges in the field will be discussed.

References

[1]

David Carmel, Naama Zwerdling, Ido Guy, Shila Ofek-Koifman, Nadav Har'el, Inbal Ronen, Erel Uziel, Sivan Yogev, and Sergey Chernov. Personalized social search based on the user's social network. In Proceeding CIKM, pages 1227--1236. ACM, 2009.

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Wen-Yen Chen, Jon-Chyuan Chu, Junyi Luan, Hongjie Bai, Yi Wang, and Edward Y. Chang. Collaborative filtering for orkut communities: discovery of user latent behavior. In Proceedings of WWW, pages 681--690. ACM, 2009.

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[3]

Ido Guy, Inbal Ronen, and Eric Wilcox. Do you know?: recommending people to invite into your social network. In Proceedings of IUI, pages 77--86. ACM, 2009.

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Ido Guy, Naama Zwerdling, David Carmel, Inbal Ronen, Erel Uziel, Sivan Yogev, and Shila Ofek-Koifman. Personalized recommendation of social software items based on social relations. In Proceedings RecSys, pages 53--60. ACM, 2009.

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Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, and Erel Uziel. Social media recommendation based on people and tags. In Proceeding of SIGIR, pages 194--201. ACM, 2010.

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Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. Recommender Systems: An Introduction. Cambridge University Press, 2011.

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Irwin King, Michael R. Lyu, and Hao Ma. Introduction to social recommendation. In Proceedings of WWW, pages 1355--1356. ACM, 2010.

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Shilad Sen, Jesse Vig, and John Riedl. Tagommenders: connecting users to items through tags. In Proceedings of WWW, pages 671--680. ACM, 2009.

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Börkur Sigurbjörnsson and Roelof van Zwol. Flickr tag recommendation based on collective knowledge. In Proceedings of WWW, pages 327--336. ACM, 2008.

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Cited By

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Musick GDuan WNajafian SSengupta SFlathmann CKnijnenburg BMcNeese N(2024)To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/36330748:GROUP(1-28)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3633074
Khamaksorn APongpatcharatorntep DSantirojanakul SHu D(2024)Social Recommendation Framework: A Case Study of Chinese Long-Stayers in Chiang MaiJournal of Information & Knowledge Management10.1142/S021964922450039423:04Online publication date: 21-Mar-2024
https://doi.org/10.1142/S0219649224500394
Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
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Index Terms

Social recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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The prevalence of misinformation on online social media has tangible empirical connections to increasing political polarization and partisan antipathy in the United States. Ranking algorithms for social recommendation often encode broad assumptions ...
Recommending Followees Based on Content Weighted User Interest Homophily
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Information & Contributors

Information

Published In

WWW '11: Proceedings of the 20th international conference companion on World wide web

March 2011

552 pages

ISBN:9781450306379

DOI:10.1145/1963192

General Chairs:
S. Sadagopan
IIIT-Bangalore, India
,
Krithi Ramamritham
IIT-Bombay, India
,
Arun Kumar
IBM Research, India
,
M. P. Ravindra
Infosys E & R, India
,
Program Chairs:
Elisa Bertino
Purdue University, USA
,
Ravi Kumar
Yahoo! Research, USA

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
The International Institute of Information Technology Bangalore: The International Institute of Information Technology Bangalore

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2011

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Tutorial

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WWW '11

WWW '11: 20th International World Wide Web Conference

March 28 - April 1, 2011

Hyderabad, India

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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View all

Musick GDuan WNajafian SSengupta SFlathmann CKnijnenburg BMcNeese N(2024)To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/36330748:GROUP(1-28)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3633074
Khamaksorn APongpatcharatorntep DSantirojanakul SHu D(2024)Social Recommendation Framework: A Case Study of Chinese Long-Stayers in Chiang MaiJournal of Information & Knowledge Management10.1142/S021964922450039423:04Online publication date: 21-Mar-2024
https://doi.org/10.1142/S0219649224500394
Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
Rad DCuc LLile RCuc PPantea MAnta D(2023)Suspiciousness and Fast and Slow Thinking Impact on Trust in Recommender SystemsProceedings of the International Conference on Business Excellence10.2478/picbe-2023-009917:1(1103-1118)Online publication date: 14-Jul-2023
https://doi.org/10.2478/picbe-2023-0099
Kang HSoliman NLatzke MChang JBragg J(2023)ComLittee: Literature Discovery with Personal Elected Author CommitteesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581371(1-20)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581371
Duy AVan T(2023)A Recommendation Algorithm Based on User's Behaviours in Social Networks2023 RIVF International Conference on Computing and Communication Technologies (RIVF)10.1109/RIVF60135.2023.10471861(242-247)Online publication date: 23-Dec-2023
https://doi.org/10.1109/RIVF60135.2023.10471861
Hu DPongpatcharatrontep DSantirojanakul SKhamaksorn A(2022)A Social Recommendation Framework for Chinese Long-stayers in Chiang Mai2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON)10.1109/ECTIDAMTNCON53731.2022.9720413(75-80)Online publication date: 26-Jan-2022
https://doi.org/10.1109/ECTIDAMTNCON53731.2022.9720413
Berkani LBelkacem SOuafi MGuessoum A(2020)Recommendation of users in social networks: A semantic and social based classification approachExpert Systems10.1111/exsy.1263438:2Online publication date: 13-Sep-2020
https://doi.org/10.1111/exsy.12634
Van DPham VThanh T(2020)Robust Content-Based Recommendation Distribution System with Gaussian Mixture ModelAdvances in Computational Collective Intelligence10.1007/978-3-030-63119-2_17(199-211)Online publication date: 19-Nov-2020
https://doi.org/10.1007/978-3-030-63119-2_17
Berkani L(2020)A semantic and social‐based collaborative recommendation of friends in social networksSoftware: Practice and Experience10.1002/spe.282850:8(1498-1519)Online publication date: 3-Apr-2020
https://doi.org/10.1002/spe.2828
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Abstract

References

Cited By

Index Terms

Recommendations

Recommender Systems with Characterized Social Regularization

Trust and Trustworthiness in Social Recommender Systems

Recommending Followees Based on Content Weighted User Interest Homophily

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