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Dynamic Embeddings for User Profiling in Twitter

Published: 19 July 2018 Publication History

Editorial Notes

The replacement article, available as the main download on this citation page, was published on November 12, 2018. The originally published article PDF is available for download within the supplemental section on this citation page.

Abstract

In this paper, we study the problem of dynamic user profiling in Twitter. We address the problem by proposing a dynamic user and word embedding model (DUWE), a scalable black-box variational inference algorithm, and a streaming keyword diversification model (SKDM). DUWE dynamically tracks the semantic representations of users and words over time and models their embeddings in the same space so that their similarities can be effectively measured. Our inference algorithm works with a convex objective function that ensures the robustness of the learnt embeddings. SKDM aims at retrieving top-K relevant and diversified keywords to profile users' dynamic interests. Experiments on a Twitter dataset demonstrate that our proposed embedding algorithms outperform state-of-the-art non-dynamic and dynamic embedding and topic models.

Supplementary Material

p1764-liang-original (p1764-liang-original-19july2018.pdf)
This article originally published on July 19, 2018 and is available for download here.
MP4 File (zhang_user_profiling.mp4)

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    cover image ACM Other conferences
    KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
    July 2018
    2925 pages
    ISBN:9781450355520
    DOI:10.1145/3219819
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    Published: 19 July 2018

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    Author Tags

    1. dynamic model
    2. profiling
    3. word embeddings

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    • King Abdullah University of Science and Technology

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    KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2024)Dynamic Co-Embedding Model for Temporal Attributed NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.319356435:3(3488-3502)Online publication date: Mar-2024
    • (2024)Deep autoencoder architecture with outliers for temporal attributed network embeddingExpert Systems with Applications10.1016/j.eswa.2023.122596240(122596)Online publication date: Apr-2024
    • (2024)TemporalHAN: Hierarchical attention-based heterogeneous temporal network embeddingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108376133(108376)Online publication date: Jul-2024
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