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Embedding Hierarchical Structures for Venue Category Representation

Published: 22 November 2021 Publication History

Abstract

Venue categories used in location-based social networks often exhibit a hierarchical structure, together with the category sequences derived from users’ check-ins. The two data modalities provide a wealth of information for us to capture the semantic relationships between those categories. To understand the venue semantics, existing methods usually embed venue categories into low-dimensional spaces by modeling the linear context (i.e., the positional neighbors of the given category) in check-in sequences. However, the hierarchical structure of venue categories, which inherently encodes the relationships between categories, is largely untapped. In this article, we propose a venue Category Embedding Model named Hier-CEM, which generates a latent representation for each venue category by embedding the Hierarchical structure of categories and utilizing multiple types of context. Specifically, we investigate two kinds of hierarchical context based on any given venue category hierarchy and show how to model them together with the linear context collaboratively. We apply Hier-CEM to three tasks on two real check-in datasets collected from Foursquare. Experimental results show that Hier-CEM is better at capturing both semantic and sequential information inherent in venues than state-of-the-art embedding methods.

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Published In

cover image ACM Transactions on Information Systems
ACM Transactions on Information Systems  Volume 40, Issue 3
July 2022
650 pages
ISSN:1046-8188
EISSN:1558-2868
DOI:10.1145/3498357
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2021
Accepted: 01 July 2021
Revised: 01 June 2021
Received: 01 September 2020
Published in TOIS Volume 40, Issue 3

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Author Tags

  1. Venue category representation
  2. hierarchical category structure
  3. multiple context types
  4. check-in data

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  • Research-article
  • Refereed

Funding Sources

  • National Natural Science Foundation of China
  • Natural Science Foundation of Shandong Province of China
  • Scholars Program of Shandong University

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  • (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
  • (2023)A Spatial and Adversarial Representation Learning Approach for Land Use Classification with POIsACM Transactions on Intelligent Systems and Technology10.1145/362782414:6(1-25)Online publication date: 14-Nov-2023
  • (2023)TME: Tree-guided Multi-task Embedding Learning towards Semantic Venue AnnotationACM Transactions on Information Systems10.1145/358255341:4(1-24)Online publication date: 8-Apr-2023
  • (2023)POI types characterization based on geographic feature embeddingsProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577659(507-514)Online publication date: 7-Jun-2023
  • (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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  • (2022)CAVE-SCInformation Sciences: an International Journal10.1016/j.ins.2022.08.056611:C(159-172)Online publication date: 1-Sep-2022

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