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Scalable learning of users' preferences using networked data

Authors:

Mohammad Ali Abbasi,

Huan LiuAuthors Info & Claims

HT '14: Proceedings of the 25th ACM conference on Hypertext and social media

Pages 4 - 12

https://doi.org/10.1145/2631775.2631796

Published: 01 September 2014 Publication History

Abstract

Users' personal information such as their political views is important for many applications such as targeted advertisements or real-time monitoring of political opinions. Huge amounts of data generated by social media users present opportunities and challenges to study these preferences in a large scale. In this paper, we aim to infer social media users' political views when only network information is available. In particular, given personal preferences about some of the social media users, how can we infer the preferences of unobserved individuals in the same network? There are many existing solutions that address the problem of classification with networked data problem. However, networks in social media normally involve millions and even hundreds of millions of nodes, which make the scalability an important problem in inferring personal preferences in social media. To address the scalability issue, we use social influence theory to construct new features based on a combination of local and global structures of the network. Then we use these features to train classifiers and predict users' preferences. Due to the size of real-world social networks, using the entire network information is inefficient and not practical in many cases. By extracting local social dimensions, we present an efficient and scalable solution. Further, by capturing the network's global pattern, the proposed solution, balances the performance requirement between accuracy and efficiency.

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Cited By

Abbasi FMuzammal MQu QRiaz FAshraf J(2024)SNCA: Semi-Supervised Node Classification for Evolving Large Attributed GraphsBig Data Mining and Analytics10.26599/BDMA.2024.90200337:3(794-808)Online publication date: Sep-2024
https://doi.org/10.26599/BDMA.2024.9020033
Jiang WWang YLiu XChen J(2019)A New Classification Method of Signature Network Node Based on Potential Space Projection2019 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2019.00095(628-633)Online publication date: Nov-2019
https://doi.org/10.1109/ICDMW.2019.00095
Pereira FTabassum SGama Jde Amo SOliveira G(2018)Processing Evolving Social Networks for Change Detection Based on Centrality MeasuresLearning from Data Streams in Evolving Environments10.1007/978-3-319-89803-2_7(155-176)Online publication date: 29-Jul-2018
https://doi.org/10.1007/978-3-319-89803-2_7
Show More Cited By

Index Terms

Scalable learning of users' preferences using networked data
1. Mathematics of computing
  1. Information theory

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Information

Published In

cover image ACM Conferences

HT '14: Proceedings of the 25th ACM conference on Hypertext and social media

September 2014

346 pages

ISBN:9781450329545

DOI:10.1145/2631775

General Chairs:
Leo Ferres
University of Concepción, Chile
,
Gustavo Rossi
University of La Plata, Argentina
,
Program Chairs:
Virgilio Almeida
Federal University of Minas Gerais, Brazil
,
Eelco Herder
L3S Research Center, Germany

Copyright © 2014 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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 01 September 2014

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HT '14: 25th ACM Conference on Hypertext and Social Media

September 1 - 4, 2014

Santiago, Chile

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HT '14 Paper Acceptance Rate 49 of 86 submissions, 57%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Abbasi FMuzammal MQu QRiaz FAshraf J(2024)SNCA: Semi-Supervised Node Classification for Evolving Large Attributed GraphsBig Data Mining and Analytics10.26599/BDMA.2024.90200337:3(794-808)Online publication date: Sep-2024
https://doi.org/10.26599/BDMA.2024.9020033
Jiang WWang YLiu XChen J(2019)A New Classification Method of Signature Network Node Based on Potential Space Projection2019 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2019.00095(628-633)Online publication date: Nov-2019
https://doi.org/10.1109/ICDMW.2019.00095
Pereira FTabassum SGama Jde Amo SOliveira G(2018)Processing Evolving Social Networks for Change Detection Based on Centrality MeasuresLearning from Data Streams in Evolving Environments10.1007/978-3-319-89803-2_7(155-176)Online publication date: 29-Jul-2018
https://doi.org/10.1007/978-3-319-89803-2_7
Pereira FShin SShin DLencastre M(2017)User preferences dynamics on evolving social networks - learning, modeling and predictionProceedings of the Symposium on Applied Computing10.1145/3019612.3019928(1090-1091)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3019612.3019928
Tang JChang YAggarwal CLiu H(2016)A Survey of Signed Network Mining in Social MediaACM Computing Surveys10.1145/295618549:3(1-37)Online publication date: 17-Aug-2016
https://dl.acm.org/doi/10.1145/2956185
Pereira Fde Amo SGama J(2016)On Using Temporal Networks to Analyze User Preferences DynamicsDiscovery Science10.1007/978-3-319-46307-0_26(408-423)Online publication date: 21-Sep-2016
https://doi.org/10.1007/978-3-319-46307-0_26
Zarrinkalam F(2015)Semantics-Enabled User Interest MiningProceedings of the 12th European Semantic Web Conference on The Semantic Web. Latest Advances and New Domains - Volume 908810.1007/978-3-319-18818-8_54(817-828)Online publication date: 31-May-2015
https://dl.acm.org/doi/10.1007/978-3-319-18818-8_54

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