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Incremental Group-Level Popularity Prediction in Online Social Networks

Authors:

Keqin LiAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 22, Issue 1

Article No.: 20, Pages 1 - 26

https://doi.org/10.1145/3461839

Published: 14 September 2021 Publication History

Abstract

Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.

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Cited By

Gong ZShen LZhang YZhang LWang JBai GXiang Y(2024)AgrAmplifier: Defending Federated Learning Against Poisoning Attacks Through Local Update AmplificationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.333355519(1241-1250)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2023.3333555
Jiang YWang RSun JWang YYou HZhang Y(2024)Rumor Localization, Detection and Prediction in Social NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321692311:3(3168-3178)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2022.3216923
Hidayati SFiqih Thalib MMunif A(2024)The Influence of User Profile and Post Metadata on the Popularity of Image-Based Social Media: A Data Perspective2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)10.1109/ICAIIC60209.2024.10463510(806-811)Online publication date: 19-Feb-2024
https://doi.org/10.1109/ICAIIC60209.2024.10463510
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Index Terms

Incremental Group-Level Popularity Prediction in Online Social Networks
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics
2. Networks
  1. Network types
    1. Overlay and other logical network structures
      1. Online social networks
      2. Social media networks

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Information & Contributors

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 22, Issue 1

February 2022

717 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3483347

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 September 2021

Accepted: 01 April 2021

Revised: 01 March 2021

Received: 01 October 2020

Published in TOIT Volume 22, Issue 1

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NSFC
National Outstanding Youth Science Program of NSFC
Science and Technology Program of Changsha City kq
Open project of Zhejiang Lab

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Cited By

Gong ZShen LZhang YZhang LWang JBai GXiang Y(2024)AgrAmplifier: Defending Federated Learning Against Poisoning Attacks Through Local Update AmplificationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.333355519(1241-1250)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2023.3333555
Jiang YWang RSun JWang YYou HZhang Y(2024)Rumor Localization, Detection and Prediction in Social NetworkIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321692311:3(3168-3178)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2022.3216923
Hidayati SFiqih Thalib MMunif A(2024)The Influence of User Profile and Post Metadata on the Popularity of Image-Based Social Media: A Data Perspective2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)10.1109/ICAIIC60209.2024.10463510(806-811)Online publication date: 19-Feb-2024
https://doi.org/10.1109/ICAIIC60209.2024.10463510
Pilania UKumar M(2023)An Analysis of Machine Learning Techniques for Predicting and Evaluating the Popularity of Online News Articles2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE)10.1109/AECE59614.2023.10428196(186-190)Online publication date: 23-Nov-2023
https://doi.org/10.1109/AECE59614.2023.10428196
Oyewola DDada EOmotehinwa TEmebo OOluwagbemi O(2022)Application of Deep Learning Techniques and Bayesian Optimization with Tree Parzen Estimator in the Classification of Supply Chain Pricing Datasets of Health MedicationsApplied Sciences10.3390/app12191016612:19(10166)Online publication date: 10-Oct-2022
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Yu LXu XTrajcevski GZhou F(2022)Transformer-enhanced Hawkes process with decoupling training for information cascade predictionKnowledge-Based Systems10.1016/j.knosys.2022.109740255:COnline publication date: 14-Nov-2022
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