short-paper

Classifying points of interest with minimum metadata

Authors:

Giorgos Giannopoulos,

Konstantinos Alexis,

Nikos Kostagiolas,

Dimitrios SkoutasAuthors Info & Claims

LocalRec '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

Article No.: 1, Pages 1 - 4

https://doi.org/10.1145/3356994.3365504

Published: 05 November 2019 Publication History

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Abstract

In this paper, we present an approach for effectively classifying Points of Interest (POIs) that are represented only by their name and location (coordinates). Most existing approaches make the assumption that the handled POIs carry a wealth of metadata (e.g. reviews, ratings, working hours, price ranges). Consequently, such methods rely on semantically rich POI profiles and exploit them to develop correspondingly rich, and thus more accurate, POI classification models. However, in several real world scenarios, assuming the existence of such rich POI profiles is unrealistic. Contrary to existing works, we propose a method that can produce accurate category recommendations based only on the minimum amount of initially available POI metadata (name, coordinates) combined with open and straightforwardly accessible metadata drawn from OpenStreetMap. To this end, we propose a set of textual and neighbourhood-based training features, capturing POI properties as well as their relations with their spatial neighborhoods. These features are fed into several classification algorithms and are evaluated on a proprietary POI dataset of a geo-marketing company and the Yelp POI dataset.

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Cited By

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Balsebre PHuang WCong GLi YSerra ESpezzano F(2024)City Foundation Models for Learning General Purpose Representations from OpenStreetMapProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679662(87-97)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679662
Zarbakhsh NMcArdle G(2024)A Novel Framework for Spatiotemporal POI AnalysisWeb and Wireless Geographical Information Systems10.1007/978-3-031-60796-7_2(23-40)Online publication date: 9-May-2024
https://doi.org/10.1007/978-3-031-60796-7_2
Rimjhim Dandapat S(2023)Tagging Multi-Label Categories to Points of Interest From Check-In DataIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.32293387:4(1191-1204)Online publication date: Aug-2023
https://doi.org/10.1109/TETCI.2022.3229338
Show More Cited By

Index Terms

Classifying points of interest with minimum metadata
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

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Published In

LocalRec '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

November 2019

92 pages

ISBN:9781450369633

DOI:10.1145/3356994

Editors:
Panagiotis Bouros
Johannes Gutenberg University Mainz, Germany
,
Tamraparni Dasu
AT&T Labs - Research
,
Yaron Kanza
AT&T Labs - Research
,
Matthias Renz
Christian-Albrechts-Universität zu Kiel, Germany
,
Dimitris Sacharidis
Technische Universität Wien, Austria

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: 05 November 2019

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Short-paper

Conference

SIGSPATIAL '19

Sponsor:

SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5, 2019

Illinois, Chicago

Acceptance Rates

LocalRec '19 Paper Acceptance Rate 6 of 12 submissions, 50%;

Overall Acceptance Rate 17 of 26 submissions, 65%

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Cited By

View all

Balsebre PHuang WCong GLi YSerra ESpezzano F(2024)City Foundation Models for Learning General Purpose Representations from OpenStreetMapProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679662(87-97)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679662
Zarbakhsh NMcArdle G(2024)A Novel Framework for Spatiotemporal POI AnalysisWeb and Wireless Geographical Information Systems10.1007/978-3-031-60796-7_2(23-40)Online publication date: 9-May-2024
https://doi.org/10.1007/978-3-031-60796-7_2
Rimjhim Dandapat S(2023)Tagging Multi-Label Categories to Points of Interest From Check-In DataIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.32293387:4(1191-1204)Online publication date: Aug-2023
https://doi.org/10.1109/TETCI.2022.3229338
Tran VLe QPham BLuu VBui Q(2022)Large-scale Vietnamese point-of-interest classification using weak labelingFrontiers in Artificial Intelligence10.3389/frai.2022.10205325Online publication date: 9-Dec-2022
https://doi.org/10.3389/frai.2022.1020532
Sun MRen Q(2022)A new method to explore the abnormal space of urban hidden dangers under epidemic outbreak and its prevention and control: A case study of Jinan CityOpen Geosciences10.1515/geo-2022-043514:1(1356-1379)Online publication date: 25-Nov-2022
https://doi.org/10.1515/geo-2022-0435
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
Bouros PDasu TKanza YRenz MSacharidis D(2020)LocalRec 2019 workshop report: The Third ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and GeoadvertisingSIGSPATIAL Special10.1145/3383653.338366511:3(30-33)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3383653.3383665

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