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MAPS: A Multi Aspect Personalized POI Recommender System

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Tao LiAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Pages 281 - 284

https://doi.org/10.1145/2959100.2959187

Published: 07 September 2016 Publication History

Abstract

The evolution of the World Wide Web (WWW) and the smart-phone technologies have played a key role in the revolution of our daily life. The location-based social networks (LBSN) have emerged and facilitated the users to share the check-in information and multimedia contents. The Point of Interest (POI) recommendation system uses the check-in information to predict the most potential check-in locations. The different aspects of the check-in information, for instance, the geographical distance, the category, and the temporal popularity of a POI; and the temporal check-in trends, and the social (friendship) information of a user play a crucial role in an efficient recommendation. In this paper, we propose a fused recommendation model termed MAPS (Multi Aspect Personalized POI Recommender System) which will be the first in our knowledge to fuse the categorical, the temporal, the social and the spatial aspects in a single model. The major contribution of this paper are: (i) it realizes the problem as a graph of location nodes with constraints on the category and the distance aspects (i.e. the edge between two locations is constrained by a threshold distance and the category of the locations), (ii) it proposes a multi-aspect fused POI recommendation model, and (iii) it extensively evaluates the model with two real-world data sets.

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Cited By

Mauro GMinici MPugliese C(2024)A Preliminary Investigation of User- and Item-Centered Bias in POI Recommendation2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00058(277-282)Online publication date: 24-Jun-2024
https://doi.org/10.1109/MDM61037.2024.00058
Chen CHuang J(2023)Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation SystemsFuture Internet10.3390/fi1510032315:10(323)Online publication date: 28-Sep-2023
https://doi.org/10.3390/fi15100323
Wei WZhao SZou D(2023)Recommendation System: A Survey and New PerspectivesWorld Scientific Annual Review of Artificial Intelligence10.1142/S281103232330001301Online publication date: 4-May-2023
https://doi.org/10.1142/S2811032323300013
Show More Cited By

Index Terms

MAPS: A Multi Aspect Personalized POI Recommender System
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
      1. Social network analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Spatial-temporal systems
      1. Location based services

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cover image ACM Conferences

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

September 2016

490 pages

ISBN:9781450340359

DOI:10.1145/2959100

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

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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Published: 07 September 2016

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RecSys '16

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SIGCHI

RecSys '16: Tenth ACM Conference on Recommender Systems

September 15 - 19, 2016

Massachusetts, Boston, USA

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RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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18th ACM Conference on Recommender Systems

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Cited By

Mauro GMinici MPugliese C(2024)A Preliminary Investigation of User- and Item-Centered Bias in POI Recommendation2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00058(277-282)Online publication date: 24-Jun-2024
https://doi.org/10.1109/MDM61037.2024.00058
Chen CHuang J(2023)Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation SystemsFuture Internet10.3390/fi1510032315:10(323)Online publication date: 28-Sep-2023
https://doi.org/10.3390/fi15100323
Wei WZhao SZou D(2023)Recommendation System: A Survey and New PerspectivesWorld Scientific Annual Review of Artificial Intelligence10.1142/S281103232330001301Online publication date: 4-May-2023
https://doi.org/10.1142/S2811032323300013
Chen DGan PLee HLi ZKrishna VWang Q(2023)Sustaining Tourism Sector Through Domestic Tourism and AnalyticsTourism Analytics Before and After COVID-1910.1007/978-981-19-9369-5_12(199-210)Online publication date: 9-Mar-2023
https://doi.org/10.1007/978-981-19-9369-5_12
Li Y(2022)A POI Recommendation Algorithm Based on the Heterogeneous Graph Convolution NetworkScientific Programming10.1155/2022/91547122022(1-14)Online publication date: 8-Oct-2022
https://doi.org/10.1155/2022/9154712
Ren YChen WChen X(2022)Application of the BP Neural Network Model in the Coordinated Development of Tourism Economic Networks in the Guangdong-Hong Kong-Macao Greater Bay AreaComputational Intelligence and Neuroscience10.1155/2022/37266962022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3726696
Sánchez PBellogín A(2022)Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental PerspectiveACM Computing Surveys10.1145/3510409Online publication date: 14-Jan-2022
https://doi.org/10.1145/3510409
Rahmani HAliannejadi MBaratchi MCrestani F(2022)A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest RecommendationACM Transactions on Information Systems10.1145/350847840:4(1-35)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3508478
Yu DYu TWang DShen Y(2022)NGPR: A comprehensive personalized point-of-interest recommendation method based on heterogeneous graphsMultimedia Tools and Applications10.1007/s11042-022-13088-481:27(39207-39228)Online publication date: 28-Apr-2022
https://doi.org/10.1007/s11042-022-13088-4
Mo FYamana H(2022)GN-GCN: Combining Geographical Neighbor Concept with Graph Convolution Network for POI RecommendationInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_15(153-165)Online publication date: 20-Nov-2022
https://doi.org/10.1007/978-3-031-21047-1_15
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