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PTHMM: Beyond Single Specific Behavior Prediction

Published: 18 May 2015 Publication History

Abstract

Existing works on user behavior analysis mainly focus on modeling a single behavior and predicting whether a user will take an action or not. However, users' behaviors do not always happen in isolation, sometimes, different behaviors may happen simultaneously. Therefore, in this paper, we try to analyze the combination of basic behaviors, called behavioral state here, which can describes users' complex behaviors comprehensively. We propose a model, called Personal Timed Hidden Markov Model (PTHMM), to settle the problem by considering time-interval information of users' behaviors and users' personalization. The experimental result on sina-weibo demonstrates the effectiveness of the model. It also shows that users' behavioral state is affected by their historical behaviors, and the influence of historical behaviors declines with the increasing of historical time.

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Cited By

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  • (2016)Analyzing information sharing strategies of users in online social networksProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3192424.3192470(247-254)Online publication date: 18-Aug-2016
  • (2016)Analyzing information sharing strategies of users in online social networks2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2016.7752242(247-254)Online publication date: Aug-2016

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  1. PTHMM: Beyond Single Specific Behavior Prediction

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    Published In

    cover image ACM Other conferences
    WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
    May 2015
    1602 pages
    ISBN:9781450334730
    DOI:10.1145/2740908

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    • IW3C2: International World Wide Web Conference Committee

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 May 2015

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    Author Tags

    1. microblogging
    2. sequential model
    3. social media
    4. user behavior
    5. user modeling

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    • Research-article

    Funding Sources

    • National Natural Science Foundation of China

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    WWW '15
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    • IW3C2

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

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    View all
    • (2016)Analyzing information sharing strategies of users in online social networksProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3192424.3192470(247-254)Online publication date: 18-Aug-2016
    • (2016)Analyzing information sharing strategies of users in online social networks2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2016.7752242(247-254)Online publication date: Aug-2016

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