research-article

Personalized POI recommendation based on check-in data and geographical-regional influence

Authors:

Shun LiAuthors Info & Claims

ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing

Pages 128 - 133

https://doi.org/10.1145/3310986.3311034

Published: 25 January 2019 Publication History

Abstract

Nowadays, many people like to share the places they visited to their friends in the location-based social networks (LBSNs). Therefore, LBSNs have accumulated large-scale user check-in data and the availability of these data enables many location-based services to users. As a location-based service, point-of-interests (POI) recommendation can provide services to people and promote merchants. Many researchers utilize user-based collaborative filtering and geographical influence for POI recommendation. However, existing studies have two limitations: (1) when modeling user-based CF, users' POI preferences are not fully considered; (2) when modeling geographical influence, geographical features have not been explored deeply. In this paper, we propose a POI recommendation approach by improved user-based CF and geographical-regional influence. Firstly, we construct the user-POI matrix by normalized check-in frequencies, which can effectively represent user preferences. Secondly, we find that each user's check-in POIs can be divided into several regions. Accordingly, we integrate geographical influence with the regional feature to produce recommendation. Finally, we utilize a unified framework to combine improved user-based CF with geographical-regional influence for POI recommendation. Our experimental results on real-world dataset show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.

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Cited By

Zhang JLi BZhang YXu YLi H(2024)Research on the recommendation method of urban location point of interest based on DTCN-EFFN-TransformerThe Journal of Supercomputing10.1007/s11227-024-06742-181:1Online publication date: 30-Nov-2024
https://doi.org/10.1007/s11227-024-06742-1
Omura TDollack FSiriaraya PLi DTanaka KKawai YNakajima S(2023)Verification of a method for latent interest estimation based on user behavior analysis and POI attributes2023 International Conference on Computing, Networking and Communications (ICNC)10.1109/ICNC57223.2023.10074037(529-535)Online publication date: 20-Feb-2023
https://doi.org/10.1109/ICNC57223.2023.10074037
Omura TLi DSiriaraya PTanaka KKawai YNakajima S(2023)User Latent Interest Estimation in Real Space: A Comparative Analysis of Time-Series and Non-Time-Series Processing Algorithms2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386563(2131-2138)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386563
Show More Cited By

Index Terms

Personalized POI recommendation based on check-in data and geographical-regional influence
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering

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ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing

January 2019

268 pages

ISBN:9781450366120

DOI:10.1145/3310986

Copyright © 2019 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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Association for Computing Machinery

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Publication History

Published: 25 January 2019

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ICMLSC 2019

ICMLSC 2019: 2019 the 3rd International Conference on Machine Learning and Soft Computing

January 25 - 28, 2019

Da Lat, Viet Nam

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Cited By

Zhang JLi BZhang YXu YLi H(2024)Research on the recommendation method of urban location point of interest based on DTCN-EFFN-TransformerThe Journal of Supercomputing10.1007/s11227-024-06742-181:1Online publication date: 30-Nov-2024
https://doi.org/10.1007/s11227-024-06742-1
Omura TDollack FSiriaraya PLi DTanaka KKawai YNakajima S(2023)Verification of a method for latent interest estimation based on user behavior analysis and POI attributes2023 International Conference on Computing, Networking and Communications (ICNC)10.1109/ICNC57223.2023.10074037(529-535)Online publication date: 20-Feb-2023
https://doi.org/10.1109/ICNC57223.2023.10074037
Omura TLi DSiriaraya PTanaka KKawai YNakajima S(2023)User Latent Interest Estimation in Real Space: A Comparative Analysis of Time-Series and Non-Time-Series Processing Algorithms2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386563(2131-2138)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386563
Vahidnia M(2022)Point-of-interest recommendation in location-based social networks based on collaborative filtering and spatial kernel weightingGeocarto International10.1080/10106049.2022.208662637:26(13949-13972)Online publication date: 15-Jun-2022
https://doi.org/10.1080/10106049.2022.2086626
Liu SYang LZheng WXiao YLiu L(2022)An ensemble learning model for preference-geographical aware point-of interest recommendationApplied Intelligence10.1007/s10489-022-04035-952:12(13763-13780)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10489-022-04035-9
Zhan HDing ZChang MLiu X(2021)POI Recommend for Deep Neural Network Based on Explicit and Implicit Feature Joint2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI52525.2021.00057(352-358)Online publication date: Nov-2021
https://doi.org/10.1109/ICTAI52525.2021.00057
Werneck HSilva NViana MPereira AMourão FRocha L(2021)Points of Interest recommendations: Methods, evaluation, and future directionsInformation Systems10.1016/j.is.2021.101789101(101789)Online publication date: Nov-2021
https://doi.org/10.1016/j.is.2021.101789
Werneck HSilva NViana MMourão FPereira ARocha Lde Salles Soares Neto C(2020)A Survey on Point-of-Interest Recommendation in Location-based Social NetworksProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3430970(185-192)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428658.3430970
Elangovan RVairavasundaram SVaradarajan VRavi L(2020)Location‐based social network recommendations with computational intelligence‐based similarity computation and user check‐in behaviorConcurrency and Computation: Practice and Experience10.1002/cpe.610633:22Online publication date: 24-Nov-2020
https://doi.org/10.1002/cpe.6106

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