short-paper

Modeling Infinite Topics on Social Behavior Data with Spatio-temporal Dependence

Authors:

Peng Wang,

Peng Zhang,

Chuan Zhou,

Zhao Li,

Guo LiAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 1919 - 1922

https://doi.org/10.1145/2806416.2806635

Published: 17 October 2015 Publication History

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Abstract

The problem of modeling topics on user behavior data in social networks has been widely studied in social marketing and social emotion analysis, where latent topic models are commonly used as the solutions. The user behavior data are highly related in time and space, which demands new latent topic models that consider both temporal and spatial distances. However, existing topic models either fail to model these two factors simultaneously, or cannot handle the high order dependence among user behaviors. In this paper we present a new nonparametric Bayesian model Time and Space Dependent Chinese Restaurant Processes (TSD-CRP for short). TSD-CRP can auto-select the number of topics and model high-order temporal and spatial dependence behind user behavior data. Empirical results on real-world data sets demonstrate the effectiveness of the proposed method.

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Cited By

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Liu YAo XDong LZhang CWang JHe Q(2023)Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding : Extended Abstract2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00334(3815-3816)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00334
Li ZDing DLiu XZhang PWu YMa L(2021)TTNet: Tabular Transfer Network for Few-samples PredictionIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3493940(293-301)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3486622.3493940
Liu YAo XDong LZhang CWang JHe Q(2020)Spatiotemporal Activity Modeling via Hierarchical Cross-Modal EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.2983892(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2020.2983892
Show More Cited By

Index Terms

Modeling Infinite Topics on Social Behavior Data with Spatio-temporal Dependence
1. Mathematics of computing
  1. Probability and statistics
    1. Nonparametric statistics

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Published In

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2015

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Funding Sources

National Nature Science Foundation of China
Australia ARC Discovery Project
Strategic Leading Science and Technology Projects of CAS
973 project

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CIKM'15

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Liu YAo XDong LZhang CWang JHe Q(2023)Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding : Extended Abstract2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00334(3815-3816)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00334
Li ZDing DLiu XZhang PWu YMa L(2021)TTNet: Tabular Transfer Network for Few-samples PredictionIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3493940(293-301)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3486622.3493940
Liu YAo XDong LZhang CWang JHe Q(2020)Spatiotemporal Activity Modeling via Hierarchical Cross-Modal EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.2983892(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2020.2983892
Li RZüfle AZhao LLamprianidis G(2017)Modeling and Prediction of People's Needs (Vision Paper)Proceedings of the 1st ACM SIGSPATIAL Workshop on Analytics for Local Events and News10.1145/3148044.3148047(1-4)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3148044.3148047
Zhang CYuan QHan J(2017)Bringing Semantics to Spatiotemporal Data Mining: Challenges, Methods, and Applications2017 IEEE 33rd International Conference on Data Engineering (ICDE)10.1109/ICDE.2017.210(1455-1458)Online publication date: Apr-2017
https://doi.org/10.1109/ICDE.2017.210
Guo JGong ZMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)A Nonparametric Model for Event Discovery in the Geospatial-Temporal SpaceProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983790(499-508)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983790

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