research-article

Who should share what?: item-level social influence prediction for users and posts ranking

Authors:

Lifeng SunAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 185 - 194

https://doi.org/10.1145/2009916.2009945

Published: 24 July 2011 Publication History

Abstract

People and information are two core dimensions in a social network. People sharing information (such as blogs, news, albums, etc.) is the basic behavior. In this paper, we focus on predicting item-level social influence to answer the question Who should share What, which can be extended into two information retrieval scenarios: (1) Users ranking: given an item, who should share it so that its diffusion range can be maximized in a social network; (2) Web posts ranking: given a user, what should she share to maximize her influence among her friends. We formulate the social influence prediction problem as the estimation of a user-post matrix, in which each entry represents the strength of influence of a user given a web post. We propose a Hybrid Factor Non-Negative Matrix Factorization (HF-NMF) approach for item-level social influence modeling, and devise an efficient projected gradient method to solve the HF-NMF problem. Intensive experiments are conducted and demonstrate the advantages and characteristics of the proposed method.

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Cited By

Malik SDhand GSheoran KJatain DGarg V(2024)Tri-factorized Modular Hypergraph Autoencoder for Multimodal Semantic AnalysisSN Computer Science10.1007/s42979-024-03210-85:7Online publication date: 9-Sep-2024
https://doi.org/10.1007/s42979-024-03210-8
Bao SXu QYang ZCao XHuang Q(2023)Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative SamplingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.314109545:1(1017-1035)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TPAMI.2022.3141095
Surolia AMehta S(2023)Optimal Feature Selection for Retweet Prediction in Indian Election2023 Second International Conference on Informatics (ICI)10.1109/ICI60088.2023.10421159(1-5)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICI60088.2023.10421159
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Index Terms

Who should share what?: item-level social influence prediction for users and posts ranking
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Copyright © 2011 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

Publication History

Published: 24 July 2011

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SIGIR '11

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SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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

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Cited By

Malik SDhand GSheoran KJatain DGarg V(2024)Tri-factorized Modular Hypergraph Autoencoder for Multimodal Semantic AnalysisSN Computer Science10.1007/s42979-024-03210-85:7Online publication date: 9-Sep-2024
https://doi.org/10.1007/s42979-024-03210-8
Bao SXu QYang ZCao XHuang Q(2023)Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative SamplingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.314109545:1(1017-1035)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TPAMI.2022.3141095
Surolia AMehta S(2023)Optimal Feature Selection for Retweet Prediction in Indian Election2023 Second International Conference on Informatics (ICI)10.1109/ICI60088.2023.10421159(1-5)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICI60088.2023.10421159
Xiao YZhu YHe WHuang M(2023)Influence prediction model for marketing campaigns on e-commerce platformsExpert Systems with Applications10.1016/j.eswa.2022.118575211(118575)Online publication date: Jan-2023
https://doi.org/10.1016/j.eswa.2022.118575
Shen HWang BLiu XZhang X(2022)Social Spammer Detection via Convex Nonnegative Matrix FactorizationIEEE Access10.1109/ACCESS.2022.317184610(91192-91202)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3171846
Chakraborty SHoque MRahman Jeem NBiswas MBardhan DLobaton E(2021)Fashion Recommendation Systems, Models and Methods: A ReviewInformatics10.3390/informatics80300498:3(49)Online publication date: 26-Jul-2021
https://doi.org/10.3390/informatics8030049
Li HXia CWang TWen SChen CXiang Y(2021)Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and TechniquesACM Computing Surveys10.1145/348527355:1(1-51)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1145/3485273
Weber PLudwig TBrodesser SGrönewald LKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)“It's a Kind of Art!”: Understanding Food Influencers as Influential Content CreatorsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445607(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445607
Mahajan PKaur P(2021)Harnessing user’s social influence and IoT data for personalized event recommendation in event-based social networksSocial Network Analysis and Mining10.1007/s13278-021-00722-611:1Online publication date: 27-Jan-2021
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Ju CGu QFang YBao F(2020)Research on User Influence Model Integrating Personality Traits under Strong ConnectionSustainability10.3390/su1206221712:6(2217)Online publication date: 12-Mar-2020
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