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  • Discovering Interpretable Geo-Social Communities for User Behavior Prediction

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    YinPUB2335.pdf (1.101Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Yin, Hongzhi
    Hu, Zhiting
    Zhou, Xiaofang
    Wang, Hao
    Zheng, Kai
    Nguyen, Quoc Viet Hung
    Sadiq, Shazia
    Griffith University Author(s)
    Nguyen, Henry
    Year published
    2016
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    Abstract
    Social community detection is a growing field of interest in the area of social network applications, and many approaches have been developed, including graph partitioning, latent space model, block model and spectral clustering. Most existing work purely focuses on network structure information which is, however, often sparse, noisy and lack of interpretability. To improve the accuracy and interpretability of community discovery, we propose to infer users' social communities by incorporating their spatiotemporal data and semantic information. Technically, we propose a unified probabilistic generative model, User-Community-Geo-Topic ...
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    Social community detection is a growing field of interest in the area of social network applications, and many approaches have been developed, including graph partitioning, latent space model, block model and spectral clustering. Most existing work purely focuses on network structure information which is, however, often sparse, noisy and lack of interpretability. To improve the accuracy and interpretability of community discovery, we propose to infer users' social communities by incorporating their spatiotemporal data and semantic information. Technically, we propose a unified probabilistic generative model, User-Community-Geo-Topic (UCGT), to simulate the generative process of communities as a result of network proximities, spatiotemporal co-occurrences and semantic similarity. With a well-designed multi-component model structure and a parallel inference implementation to leverage the power of multicores and clusters, our UCGT model is expressive while remaining efficient and scalable to growing large-scale geo-social networking data. We deploy UCGT to two application scenarios of user behavior predictions: check-in prediction and social interaction prediction. Extensive experiments on two large-scale geo-social networking datasets show that UCGT achieves better performance than existing state-of-the-art comparison methods.
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    Conference Title
    Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering
    DOI
    https://doi.org/10.1109/ICDE.2016.7498303
    Copyright Statement
    © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Database Management
    Publication URI
    http://hdl.handle.net/10072/348007
    Collection
    • Conference outputs

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