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Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation

Published: 26 October 2021 Publication History

Abstract

Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the users’ visiting preference probabilities from the historical check-ins in batch. However, in practice, when users’ behaviors are updated in real-time, it is often cost-inhibitive to re-estimate and updates users’ visiting preference using the same batch methods due to the number of check-ins. Moreover, an important nature of users’ movement patterns is that users are more attracted to an area where have dense locations with same categories for conducting specific behaviors. In this paper, we propose a location recommendation method called GeoRTGA by utilizing the real time user behaviors and geographical attractions to tackle the problems. GeoRTGA contains two sub-models: real time behavior recommendation model and attraction-based spatial model. The real time behavior recommendation model aims to recommend real-time possible behaviors which users prefer to visit, and the attraction-based spatial model is built to discover the category-based spatial and individualized spatial patterns based on the geographical information of locations and corresponding location categories and check-in numbers. Experiments are conducted on four public real-world check-in datasets, which show that the proposed GeoRTGA outperforms the five existing location recommendation methods.

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  1. Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation

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    cover image ACM Transactions on Spatial Algorithms and Systems
    ACM Transactions on Spatial Algorithms and Systems  Volume 8, Issue 1
    March 2022
    184 pages
    ISSN:2374-0353
    EISSN:2374-0361
    DOI:10.1145/3488003
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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 October 2021
    Accepted: 01 August 2021
    Revised: 01 June 2021
    Received: 01 July 2020
    Published in TSAS Volume 8, Issue 1

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    Author Tags

    1. Location recommendation
    2. location-based services
    3. random walk with restart
    4. geographical information

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    • National Natural Science Foundation of China
    • Natural Sciences and Engineering Research Council of Canada

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