short-paper

Joint Modeling of Users' Interests and Mobility Patterns for Point-of-Interest Recommendation

Authors:

Xiaofang ZhouAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 819 - 822

https://doi.org/10.1145/2733373.2806339

Published: 13 October 2015 Publication History

Abstract

Point-of-Interest (POI) recommendation has become an important means to help people discover interesting places, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, Topic-Region Model (TRM), to simultaneously discover the semantic, temporal and spatial patterns of users' check-in activities, and to model their joint effect on users' decision-making for POIs. We conduct extensive experiments to evaluate the performance of our TRM on two real large-scale datasets, and the experimental results clearly demonstrate that TRM outperforms the state-of-art methods.

References

[1]

G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734 -- 749, june 2005.

Digital Library

[2]

Z. Cheng, J. Caverlee, K. Lee, and D. Z. Sui. Exploring millions of footprints in location sharing services. In ICWSM, 2011.

[3]

G. Ference, M. Ye, and W.-C. Lee. Location recommendation for out-of-town users in location-based social networks. In CIKM, pages 721--726, 2013.

Digital Library

[4]

H. Gao, J. Tang, X. Hu, and H. Liu. Exploring temporal effects for location recommendation on location-based social networks. In RecSys, pages 93--100, 2013.

Digital Library

[5]

J. J. Levandoski, M. Sarwat, A. Eldawy, and M. F. Mokbel. Lars: A location-aware recommender system. In ICDE, pages 450--461, 2012.

Digital Library

[6]

D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, and Y. Rui. Geomf: Joint geographical modeling and matrix factorization for point-of-interest recommendation. In KDD, pages 831--840, 2014.

Digital Library

[7]

M. Lichman and P. Smyth. Modeling human location data with mixtures of kernel densities. In KDD, pages 35--44, 2014.

Digital Library

[8]

B. Liu, Y. Fu, Z. Yao, and H. Xiong. Learning geographical preferences for point-of-interest recommendation. In KDD, pages 1043--1051, 2013.

Digital Library

[9]

B. Liu and H. Xiong. Point-of-interest recommendation in location based social networks with topic and location awareness. In SDM, pages 396--404, 2013.

[10]

D. Mok, B. Wellman, and J. Carrasco. Does distance matter in the age of the internet? Urban Studies, 47(13):2747--2783, 2010.

[11]

S. Scellato, A. Noulas, R. Lambiotte, and C. Mascolo. Socio-spatial properties of online location-based social networks. In ICWSM, 2011.

[12]

M. Ye, D. Shou, W.-C. Lee, P. Yin, and K. Janowicz. On the semantic annotation of places in location-based social networks. In KDD, pages 520--528, 2011.

Digital Library

[13]

M. Ye, P. Yin, and W.-C. Lee. Location recommendation for location-based social networks. In GIS, pages 458--461, 2010.

Digital Library

[14]

M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR, pages 325--334, 2011.

Digital Library

[15]

H. Yin, B. Cui, L. Chen, Z. Hu, and C. Zhang. Modeling location-based user rating profiles for personalized recommendation. ACM Trans. Knowl. Discov. Data, 9(3):19:1--19:41, Apr. 2015.

Digital Library

[16]

H. Yin, B. Cui, Y. Sun, Z. Hu, and L. Chen. Lcars: A spatial item recommender system. ACM Trans. Inf. Syst., 32(3):11:1--11:37, July 2014.

Digital Library

[17]

H. Yin, Y. Sun, B. Cui, Z. Hu, and L. Chen. Lcars: A location-content-aware recommender system. In KDD, pages 221--229, 2013.

Digital Library

[18]

Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Time-aware point-of-interest recommendation. In SIGIR, pages 363--372, 2013.

Digital Library

Cited By

Chen LYuan WChen TYe GHung NYin H(2024)Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided DiffusionACM Transactions on Information Systems10.1145/366608842:6(1-26)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3666088
Long JYe GChen TWang YWang MYin HBaeza-Yates RBonchi F(2024)Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671743(2026-2036)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671743
Zheng RQu LChen TCui LShi YYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645696(3930-3939)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645696
Show More Cited By

Index Terms

Joint Modeling of Users' Interests and Mobility Patterns for Point-of-Interest Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

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Funding Sources

the National Natural Science Foundation of China
Australian Research Council
973 program of China

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MM '15

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SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

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MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 995 of 4,171 submissions, 24%

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The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

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Cited By

Chen LYuan WChen TYe GHung NYin H(2024)Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided DiffusionACM Transactions on Information Systems10.1145/366608842:6(1-26)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3666088
Long JYe GChen TWang YWang MYin HBaeza-Yates RBonchi F(2024)Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671743(2026-2036)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671743
Zheng RQu LChen TCui LShi YYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645696(3930-3939)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645696
Long JChen TYe GZheng KNguyen QYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Physical Trajectory Inference Attack and Defense in Decentralized POI RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645410(3379-3387)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645410
Guo LLu ZYu JNguyen QYin HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Prompt-enhanced Federated Content Representation Learning for Cross-domain RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645337(3139-3149)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645337
Zhang SYuan WYin H(2024)Comprehensive Privacy Analysis on Federated Recommender System Against Attribute Inference AttacksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329560136:3(987-999)Online publication date: Mar-2024
https://doi.org/10.1109/TKDE.2023.3295601
Yu JYin HXia XChen TLi JHuang Z(2024)Self-Supervised Learning for Recommender Systems: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328290736:1(335-355)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3282907
Cai HLi FFeng C(2024)FedRecTID: A General Robust Federated Recommendation Framework Based on Target Items Detection2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650323(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650323
Yuan WQu LCui LTong YZhou XYin H(2024)HeteFedRec: Federated Recommender Systems with Model Heterogeneity2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00109(1324-1337)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00109
Yuan WYuan SYang CQuoc Viet hung NYin H(2023)Manipulating Visually Aware Federated Recommender Systems and Its CountermeasuresACM Transactions on Information Systems10.1145/363000542:3(1-26)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3630005
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