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Characterizing user interest using heterogeneous media

Authors:

Jonghyun Han,

Hyunju LeeAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 289 - 290

https://doi.org/10.1145/2567948.2577346

Published: 07 April 2014 Publication History

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Abstract

It is often hard to accurately estimate interests of social media users because their messages do not have additional information, such as a category. In this paper, we propose an approach that estimates user interest from social media to provide personalized services. Our approach employs heterogeneous media to map social media onto categories. To describe the categories, we propose a hybrid method that integrates a topic model with TF-ICF for extracting both explicitly presented and implicitly latent features. Our evaluation result shows that it gives the highest performance, compared to other approaches. Thus, we expect that the proposed approach is helpful in advancing personalization of social media services.

References

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[2]

J. Han and H. Lee. Analyzing social media friendship for personalization. In Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media, pages 1--2. ACM, 2012.

Digital Library

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[3]

J. Han, X. Xie, and W. Woo. Context-based microblog browsing for mobile users. Journal of Ambient Intelligence and Smart Environments, 5(1):89--104, 2013.

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Cited By

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Kumar ASpaniol MBaeza-Yates RWeller KPortela MSeneviratne OWeber IYasseri TBon ASrinivas SIbáñez L(2022)There is a fine Line between Personalization and Surveillance: Semantic User Interest Tracing via Entity-level AnalyticsProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531592(22-33)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1145/3501247.3531592
Dhelim SNing HAung NHuang RMa J(2021)Personality-Aware Product Recommendation System Based on User Interests Mining and Metapath DiscoveryIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.30370408:1(86-98)Online publication date: Feb-2021
https://doi.org/10.1109/TCSS.2020.3037040
Kang JChoi HLee H(2019)Deep recurrent convolutional networks for inferring user interests from social mediaJournal of Intelligent Information Systems10.1007/s10844-018-0534-352:1(191-209)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10844-018-0534-3
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Index Terms

Characterizing user interest using heterogeneous media
1. Human-centered computing
  1. Interaction design
    1. Interaction design process and methods
      1. User centered design
2. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

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

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Poster

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WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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

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Cited By

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Kumar ASpaniol MBaeza-Yates RWeller KPortela MSeneviratne OWeber IYasseri TBon ASrinivas SIbáñez L(2022)There is a fine Line between Personalization and Surveillance: Semantic User Interest Tracing via Entity-level AnalyticsProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531592(22-33)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1145/3501247.3531592
Dhelim SNing HAung NHuang RMa J(2021)Personality-Aware Product Recommendation System Based on User Interests Mining and Metapath DiscoveryIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.30370408:1(86-98)Online publication date: Feb-2021
https://doi.org/10.1109/TCSS.2020.3037040
Kang JChoi HLee H(2019)Deep recurrent convolutional networks for inferring user interests from social mediaJournal of Intelligent Information Systems10.1007/s10844-018-0534-352:1(191-209)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10844-018-0534-3
Alhozaimi AAlmishari M(2018)Arabic Twitter Profiling For Arabic-Speaking Users2018 21st Saudi Computer Society National Computer Conference (NCC)10.1109/NCG.2018.8593031(1-6)Online publication date: Apr-2018
https://doi.org/10.1109/NCG.2018.8593031
Song SMeng YShi ZZheng ZChen H(2017)A simple yet effective method for summarizing microblogging users with their representative tweets2017 International Conference on Asian Language Processing (IALP)10.1109/IALP.2017.8300605(310-313)Online publication date: Dec-2017
https://doi.org/10.1109/IALP.2017.8300605
Jin ZLiu RLi QZeng DZhan YWang L(2016)Predicting user's multi-interests with network embedding in health-related topics2016 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2016.7727520(2568-2575)Online publication date: Jul-2016
https://doi.org/10.1109/IJCNN.2016.7727520
Song SMeng YDesai BToyoma M(2015)Detecting Representative Tweets of Microblogging UsersProceedings of the Eighth International C* Conference on Computer Science & Software Engineering10.1145/2790798.2790815(110-112)Online publication date: 13-Jul-2015
https://dl.acm.org/doi/10.1145/2790798.2790815

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Characterizing the interests of social media users

Enriching one-class collaborative filtering with content information from social media

User Interest and Topic Detection for Personalized Recommendation