research-article

Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modeling

Authors:

Carsten EickhoffAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 36, Issue 3

Article No.: 32, Pages 1 - 33

https://doi.org/10.1145/3182165

Published: 13 March 2018 Publication History

Abstract

Many social network applications depend on robust representations of spatio-temporal data. In this work, we present an embedding model based on feed-forward neural networks which transforms social media check-ins into dense feature vectors encoding geographic, temporal, and functional aspects for modeling places, neighborhoods, and users. We employ the embedding model in a variety of applications including location recommendation, urban functional zone study, and crime prediction. For location recommendation, we propose a Spatio-Temporal Embedding Similarity algorithm (STES) based on the embedding model.

In a range of experiments on real life data collected from Foursquare, we demonstrate our model’s effectiveness at characterizing places and people and its applicability in aforementioned problem domains. Finally, we select eight major cities around the globe and verify the robustness and generality of our model by porting pre-trained models from one city to another, thereby alleviating the need for costly local training.

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Cited By

Jia HChen MHuang WZhao KGong YLarson K(2024)Learning hierarchy-enhanced POI category representations using disentangled mobility sequencesProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/231(2090-2098)Online publication date: 3-Aug-2024
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Zhang TLiu HGeng XYu G(2023)Event-Based Probabilistic Embedding for POI RecommendationApplied Sciences10.3390/app1303123613:3(1236)Online publication date: 17-Jan-2023
https://doi.org/10.3390/app13031236
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Index Terms

Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modeling
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 36, Issue 3

July 2018

402 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3146384

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2018 ACM.

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Publication History

Published: 13 March 2018

Accepted: 01 January 2018

Revised: 01 January 2018

Received: 01 July 2017

Published in TOIS Volume 36, Issue 3

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Jia HChen MHuang WZhao KGong YLarson K(2024)Learning hierarchy-enhanced POI category representations using disentangled mobility sequencesProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/231(2090-2098)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/231
Zhang TLiu HGeng XYu G(2023)Event-Based Probabilistic Embedding for POI RecommendationApplied Sciences10.3390/app1303123613:3(1236)Online publication date: 17-Jan-2023
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https://dl.acm.org/doi/10.1145/3627824
Bing JChen MYang MHuang WGong YNie L(2023)Pre-Trained Semantic Embeddings for POI Categories Based on Multiple ContextsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.321885135:9(8893-8904)Online publication date: 1-Sep-2023
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