research-article

Social Link Prediction in Online Social Tagging Systems

Authors:

Charalampos Chelmis and

Viktor K. PrasannaAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 31, Issue 4

Article No.: 20, Pages 1 - 27

https://doi.org/10.1145/2516891

Published: 01 November 2013 Publication History

Abstract

Social networks have become a popular medium for people to communicate and distribute ideas, content, news, and advertisements. Social content annotation has naturally emerged as a method of categorization and filtering of online information. The unrestricted vocabulary users choose from to annotate content has often lead to an explosion of the size of space in which search is performed. In this article, we propose latent topic models as a principled way of reducing the dimensionality of such data and capturing the dynamics of collaborative annotation process. We propose three generative processes to model latent user tastes with respect to resources they annotate with metadata. We show that latent user interests combined with social clues from the immediate neighborhood of users can significantly improve social link prediction in the online music social media site Last.fm. Most link prediction methods suffer from the high class imbalance problem, resulting in low precision and/or recall. In contrast, our proposed classification schemes for social link recommendation achieve high precision and recall with respect to not only the dominant class (nonexistence of a link), but also with respect to sparse positive instances, which are the most vital in social tie prediction.

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Garba AWu SKhalid S(2023)Federated search techniques: an overview of the trends and state of the artKnowledge and Information Systems10.1007/s10115-023-01922-665:12(5065-5095)Online publication date: 10-Jul-2023
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Social Link Prediction in Online Social Tagging Systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 31, Issue 4

November 2013

192 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2536736

Editor:
Jamie Callan
Carnegie Mellon University, USA

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Copyright © 2013 ACM.

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Publication History

Published: 01 November 2013

Accepted: 01 June 2013

Revised: 01 May 2013

Received: 01 January 2013

Published in TOIS Volume 31, Issue 4

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Garba AWu SKhalid S(2023)Federated search techniques: an overview of the trends and state of the artKnowledge and Information Systems10.1007/s10115-023-01922-665:12(5065-5095)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1007/s10115-023-01922-6
Singh ALakshmanan K(2021)PILHNB: Popularity, interests, location used hidden Naive Bayesian-based model for link prediction in dynamic social networksNeurocomputing10.1016/j.neucom.2021.02.101Online publication date: Jun-2021
https://doi.org/10.1016/j.neucom.2021.02.101
Zhao WHou YChen JZhu JYin ESu HWen J(2020)Learning Semantic Representations from Directed Social Links to Tag Microblog Users at ScaleACM Transactions on Information Systems10.1145/337755038:2(1-30)Online publication date: 7-Mar-2020
https://dl.acm.org/doi/10.1145/3377550
Zarrinkalam FPiao GFaralli SBagheri Ed'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Mining User Interests from Social MediaProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412167(3519-3520)Online publication date: 19-Oct-2020
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Wu JHuang LZhao J(2019)Operationalizing regulatory focus in the digital ageMIS Quarterly10.25300/MISQ/2019/1442043:3(745-764)Online publication date: 1-Sep-2019
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Zarrinkalam FFani HBagheri EPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Extracting, Mining and Predicting Users' Interests from Social NetworksProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331383(1407-1408)Online publication date: 18-Jul-2019
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