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Geographical Feature Extraction for Entities in Location-based Social Networks

Published: 23 April 2018 Publication History

Abstract

Location-based embedding is a fundamental problem to solve in location-based social networks (LBSN). In this paper, we propose a geographical convolutional neural tensor network (GeoCNTN) as a generic embedding model. GeoCNTN first takes the raw location data and extracts from it a more well-conditioned representation by our proposed Geo-CMeans algorithm. We then use a convolutional neural network (CNN) and an embedding structure to extract individual latent structural patterns from the preprocessed data. Finally, we apply a neural tensor network (NTN) to craft the implicitly related features we have obtained into a unified geographical feature. The advantages of our GeoCNTN mainly come from its novel neural network structure, which intrinsically offers a mechanism to extract latent structural features from the geographical data, as well as its wide applicability in various LBSN-related tasks. From two case studies, i.e. link prediction and entity classification in user-group LBSN, we evaluate the embedding efficacy of our model. Results show that GeoCNTN significantly performs better on at least two tasks, with improvement by 9% w.r.t. NDCG and 11% w.r.t. F1 score respectively, using the Meetup-USA dataset.

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  1. Geographical Feature Extraction for Entities in Location-based Social Networks

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    cover image ACM Other conferences
    WWW '18: Proceedings of the 2018 World Wide Web Conference
    April 2018
    2000 pages
    ISBN:9781450356398
    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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    • IW3C2: International World Wide Web Conference Committee

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    International World Wide Web Conferences Steering Committee

    Republic and Canton of Geneva, Switzerland

    Publication History

    Published: 23 April 2018

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    Author Tags

    1. deep learning
    2. feature embedding
    3. location-based social networks

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    • Research-article

    Funding Sources

    • National Program on Key Basic Research
    • R&D Program of STCSM

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    WWW '18
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    • IW3C2
    WWW '18: The Web Conference 2018
    April 23 - 27, 2018
    Lyon, France

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    WWW '18 Paper Acceptance Rate 170 of 1,155 submissions, 15%;
    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    • (2024)Neural networks for intelligent multilevel control of artificial and natural objects based on data fusion: A surveyInformation Fusion10.1016/j.inffus.2024.102427110(102427)Online publication date: Oct-2024
    • (2023)Classification-Labeled Continuousization and Multi-Domain Spatio-Temporal Fusion for Fine-Grained Urban Crime PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.318072635:7(6725-6738)Online publication date: 1-Jul-2023
    • (2022)Dual Subgraph-Based Graph Neural Network for Friendship Prediction in Location-Based Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/355498117:3(1-28)Online publication date: 16-Aug-2022
    • (2022)Automated synthesis of biodiversity knowledge requires better tools and standardised research outputEcography10.1111/ecog.060682022:3Online publication date: 18-Feb-2022
    • (2022)Deep Learning for Spatio-Temporal Data Mining: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.302558034:8(3681-3700)Online publication date: 1-Aug-2022
    • (2022)Semantic-Aware Privacy-Preserving Online Location Trajectory Data SharingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2022.318185517(2256-2271)Online publication date: 2022
    • (2021)A survey of location-based social networks: problems, methods, and future research directionsGeoInformatica10.1007/s10707-021-00450-1Online publication date: 24-Sep-2021
    • (2020)Modeling Personalized Out-of-Town Distances in Location Recommendation2020 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM50108.2020.00020(112-121)Online publication date: Nov-2020
    • (2019)Learning Domain Driven and Semantically Enriched Embeddings for POI ClassificationProceedings of the 16th International Symposium on Spatial and Temporal Databases10.1145/3340964.3340992(214-217)Online publication date: 19-Aug-2019
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