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Investigating topic models for social media user recommendation

Published: 28 March 2011 Publication History

Abstract

This paper presents a user recommendation system that recommends to a user new friends having similar interests. We automatically discover users' interests using Latent Dirichlet Allocation (LDA), a linguistic topic model that represents users as mixtures of topics. Our system is able to recommend friends for 4 million users with high recall, outperforming existing strategies based on graph analysis.

References

[1]
D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. JMLR, (3):993--1022, 2002.
[2]
D. Ramage, S. Dumais, and D. Liebling. Characterizing microblogs with topic models. In Proc. of ICWSM, 2010.
[3]
A. Smola and S. Narayanamurthy. An architecture for parallel topic models. In Proc. of VLDB, 2010.

Cited By

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  • (2025)Topic modelling through the bibliometrics lens and its techniqueArtificial Intelligence Review10.1007/s10462-024-11011-x58:3Online publication date: 6-Jan-2025
  • (2024)What Makes Programmers Laugh? Exploring the Submissions of the Subreddit r/ProgrammerHumor.Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686696(371-381)Online publication date: 24-Oct-2024
  • (2024)Topic Modeling-Driven Feature Engineering to Enhance Clickbait Detection in Social Networks2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA62523.2024.10786672(1-8)Online publication date: 17-Jul-2024
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  1. Investigating topic models for social media user recommendation

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    Published In

    cover image ACM Other conferences
    WWW '11: Proceedings of the 20th international conference companion on World wide web
    March 2011
    552 pages
    ISBN:9781450306379
    DOI:10.1145/1963192

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 March 2011

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    Author Tags

    1. LDA
    2. social media
    3. topic models
    4. user recommendation

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    WWW '11
    WWW '11: 20th International World Wide Web Conference
    March 28 - April 1, 2011
    Hyderabad, India

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

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    Cited By

    View all
    • (2025)Topic modelling through the bibliometrics lens and its techniqueArtificial Intelligence Review10.1007/s10462-024-11011-x58:3Online publication date: 6-Jan-2025
    • (2024)What Makes Programmers Laugh? Exploring the Submissions of the Subreddit r/ProgrammerHumor.Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686696(371-381)Online publication date: 24-Oct-2024
    • (2024)Topic Modeling-Driven Feature Engineering to Enhance Clickbait Detection in Social Networks2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA62523.2024.10786672(1-8)Online publication date: 17-Jul-2024
    • (2024)Topic Modelling of Short Texts in the Health Domain using LDA and Bard2024 Conference on Information Communications Technology and Society (ICTAS)10.1109/ICTAS59620.2024.10507116(82-87)Online publication date: 7-Mar-2024
    • (2024)Enhancing Clickbait Detection with Cross-Modal Topic Modeling in Social Networks2024 7th International Conference on Information and Computer Technologies (ICICT)10.1109/ICICT62343.2024.00053(289-294)Online publication date: 15-Mar-2024
    • (2023)Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and OmicronPLOS ONE10.1371/journal.pone.028868118:8(e0288681)Online publication date: 1-Aug-2023
    • (2023)Understanding Emotional Disclosure via Diary-keeping in Quarantine on Social MediaProceedings of the Eleventh International Symposium of Chinese CHI10.1145/3629606.3629623(169-181)Online publication date: 13-Nov-2023
    • (2022)Affective Signals in a Social Media Recommender SystemProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539054(2831-2841)Online publication date: 14-Aug-2022
    • (2022)Robust Self-Supervised Structural Graph Neural Network for Social Network PredictionProceedings of the ACM Web Conference 202210.1145/3485447.3512182(1352-1361)Online publication date: 25-Apr-2022
    • (2021)Microbloggers’ interest inference using a subgraph streamIntelligent Data Analysis10.3233/IDA-19504225:2(397-417)Online publication date: 4-Mar-2021
    • Show More Cited By

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