research-article

A Spatial-Temporal Topic Model for the Semantic Annotation of POIs in LBSNs

Authors:

Bin LuoAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 8, Issue 1

Article No.: 12, Pages 1 - 24

https://doi.org/10.1145/2905373

Published: 25 July 2016 Publication History

Abstract

Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most POIs in location-based social networks (LBSNs) are either tag-missing or tag-incomplete. This article aims to develop semantic annotation techniques to automatically infer tags for POIs. We first analyze two LBSN datasets and observe that there are two types of tags, category-related ones and sentimental ones, which have unique characteristics. Category-related tags are hierarchical, whereas sentimental ones are category-aware. All existing related work has adopted classification methods to predict high-level category-related tags in the hierarchy, but they cannot apply to infer either low-level category tags or sentimental ones.

In light of this, we propose a latent-class probabilistic generative model, namely the spatial-temporal topic model (STM), to infer personal interests, the temporal and spatial patterns of topics/semantics embedded in users’ check-in activities, the interdependence between category-topic and sentiment-topic, and the correlation between sentimental tags and rating scores from users’ check-in and rating behaviors. Then, this learned knowledge is utilized to automatically annotate all POIs with both category-related and sentimental tags in a unified way. We conduct extensive experiments to evaluate the performance of the proposed STM on a real large-scale dataset. The experimental results show the superiority of our proposed STM, and we also observe that the real challenge of inferring category-related tags for POIs lies in the low-level ones of the hierarchy and that the challenge of predicting sentimental tags are those with neutral ratings.

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Gupta AShanker U(2024)Caching in Location Based Services: Approaches, Challenges and Emerging TrendsWireless Personal Communications: An International Journal10.1007/s11277-024-11132-0135:3(1581-1615)Online publication date: 1-Apr-2024
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Zhang DXu RHuang WZhao KChen MElkind E(2023)Towards an integrated view of semantic annotation for POIs with spatial and textual informationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/271(2441-2449)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/271
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Index Terms

A Spatial-Temporal Topic Model for the Semantic Annotation of POIs in LBSNs
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Information retrieval
  2. Information systems applications

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 8, Issue 1

January 2017

363 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2973184

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2016 ACM.

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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Publication History

Published: 25 July 2016

Accepted: 01 March 2016

Revised: 01 February 2016

Received: 01 August 2015

Published in TIST Volume 8, Issue 1

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Natural Science Foundation of China
Australian Research Council
Natural Science Foundation of Jiangsu Province, China
National Basic Research Program of China (973 Program)

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Gupta AShanker U(2024)Caching in Location Based Services: Approaches, Challenges and Emerging TrendsWireless Personal Communications: An International Journal10.1007/s11277-024-11132-0135:3(1581-1615)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11277-024-11132-0
Zhang DXu RHuang WZhao KChen MElkind E(2023)Towards an integrated view of semantic annotation for POIs with spatial and textual informationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/271(2441-2449)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/271
Xu RHuang WZhao JChen MNie L(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
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Xu RChen MGong YLiu YYu XNie L(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
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Yang JHan GYang DLiu JXiao YXu XWu BNi SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)M3PT: A Multi-Modal Model for POI TaggingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599862(5382-5392)Online publication date: 6-Aug-2023
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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
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