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Inferring Lifetime Status of Point-of-Interest: A Multitask Multiclass Approach

Authors:

Bin GuoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 1

Article No.: 10, Pages 1 - 27

https://doi.org/10.1145/3369799

Published: 03 February 2020 Publication History

Abstract

A Point-of-Interest (POI) refers to a specific location that people may find useful or interesting. In modern cities, a large number of POIs emerge, grow, stabilize for a period, then finally disappear. The stages (e.g., emerge and grow) in this process are called lifetime statuses of a POI. While a large body of research has been devoted to identifying and recommending POIs, there are few studies on inferring the lifetime status of POIs. Indeed, the predictive analytics of POI lifetime status can be valuable for various tasks, such as urban planning, business site selection, and real estate appraisal. In this article, we propose a multitask learning approach, named inferring POI lifetime status, to inferring the POI lifetime status with multifaceted data sources. Specifically, we first define three types of POI lifetime status, i.e., booming, decaying, and stable. Then, we formulate a serial classification problem to predict the sequential/successive lifetime statuses of POIs over time. Leveraging geographical data and human mobility data, we examine and integrate three aspects of features related to the prosperity of POIs, i.e., region popularity, region demands, and peer competitiveness. Next, as the booming/decaying POIs are relatively rare in our data, we perform stable class decomposition to alleviate the imbalance between stable POIs and booming/decaying POIs. Finally, we develop a POI lifetime status classifier by exploiting the multitask learning framework as well as the multiclass kernel-based vector machines. We perform extensive experiments using large-scale and real-world datasets of New York City. The experimental results validate the effectiveness of our approach to automatically inferring POI lifetime status.

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Cited By

You CQu HFeng CGuo L(2024)Evaluating the match between natural ecosystem service supply and cultural ecosystem service demand: Perspectives on spatiotemporal heterogeneityEnvironmental Impact Assessment Review10.1016/j.eiar.2024.107592108(107592)Online publication date: Sep-2024
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Li TXi YWang HLi YTarkoma SHui P(2023)Learning Representations of Satellite Imagery by Leveraging Point-of-InterestsACM Transactions on Intelligent Systems and Technology10.1145/358934414:4(1-32)Online publication date: 8-May-2023
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Liu HGuo QZhu HFu YZhuang FMa XXiong H(2023)Characterizing and Forecasting Urban Vibrancy Evolution: A Multi-View Graph Mining PerspectiveACM Transactions on Knowledge Discovery from Data10.1145/356868317:5(1-24)Online publication date: 28-Feb-2023
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Index Terms

Inferring Lifetime Status of Point-of-Interest: A Multitask Multiclass Approach
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 1

February 2020

325 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3375789

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Copyright © 2020 ACM.

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Publication History

Published: 03 February 2020

Accepted: 01 October 2019

Revised: 01 June 2019

Received: 01 January 2019

Published in TKDD Volume 14, Issue 1

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National Basic Research Program of China
National Natural Science Foundation of China
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Cited By

You CQu HFeng CGuo L(2024)Evaluating the match between natural ecosystem service supply and cultural ecosystem service demand: Perspectives on spatiotemporal heterogeneityEnvironmental Impact Assessment Review10.1016/j.eiar.2024.107592108(107592)Online publication date: Sep-2024
https://doi.org/10.1016/j.eiar.2024.107592
Li TXi YWang HLi YTarkoma SHui P(2023)Learning Representations of Satellite Imagery by Leveraging Point-of-InterestsACM Transactions on Intelligent Systems and Technology10.1145/358934414:4(1-32)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.1145/3589344
Liu HGuo QZhu HFu YZhuang FMa XXiong H(2023)Characterizing and Forecasting Urban Vibrancy Evolution: A Multi-View Graph Mining PerspectiveACM Transactions on Knowledge Discovery from Data10.1145/356868317:5(1-24)Online publication date: 28-Feb-2023
https://dl.acm.org/doi/10.1145/3568683
Mahmoud RHajj H(2022)Multi-objective Learning to Overcome Catastrophic Forgetting in Time-series ApplicationsACM Transactions on Knowledge Discovery from Data10.1145/350272816:6(1-20)Online publication date: 30-Jul-2022
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Shen GZhao ZKong X(2022)GCN2CDD: A Commercial District Discovery Framework via Embedding Space Clustering on Graph Convolution NetworksIEEE Transactions on Industrial Informatics10.1109/TII.2021.305193418:1(356-364)Online publication date: Jan-2022
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Ahmed DDiaz E(2022)Survey of Machine Learning Methods Applied to Urban MobilityIEEE Access10.1109/ACCESS.2022.315966810(30349-30366)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3159668
Wang RZhang XLi N(2022)Zooming into mobility to understand cities: A review of mobility-driven urban studiesCities10.1016/j.cities.2022.103939130(103939)Online publication date: Nov-2022
https://doi.org/10.1016/j.cities.2022.103939
Psyllidis AGao SHu YKim EMcKenzie GPurves RYuan MAndris C(2022)Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the futureComputational Urban Science10.1007/s43762-022-00047-w2:1Online publication date: 28-Jun-2022
https://doi.org/10.1007/s43762-022-00047-w
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Milias VPsyllidis A(2021)Assessing the influence of point-of-interest features on the classification of place categoriesComputers, Environment and Urban Systems10.1016/j.compenvurbsys.2021.10159786(101597)Online publication date: Mar-2021
https://doi.org/10.1016/j.compenvurbsys.2021.101597

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