short-paper

Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization

Authors:

Qi HeAuthors Info & Claims

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1007 - 1010

https://doi.org/10.1145/2766462.2767794

Published: 09 August 2015 Publication History

Get Access

Abstract

Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks in recent years. Compared with traditional tasks, it focuses more on personalized, context-aware recommendation results to provide better user experience. To address this new challenge, we propose a Collaborative Filtering method based on Non-negative Tensor Factorization, a generalization of the Matrix Factorization approach that exploits a high-order tensor instead of traditional User-Location matrix to model multi-dimensional contextual information. The factorization of this tensor leads to a compact model of the data which is specially suitable for context-aware POI recommendations. In addition, we fuse users' social relations as regularization terms of the factorization to improve the recommendation accuracy. Experimental results on real-world datasets demonstrate the effectiveness of our approach.

References

[1]

C. Cheng, H. Yang, I. King, and M. R. Lyu. Fused matrix factorization with geographical and social influence in location-based social networks. In Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012.

Digital Library

Google Scholar

[2]

C. Cheng, H. Yang, M. R. Lyu, and I. King. Where you like to go next: Successive point-of-interest recommendation. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, pages 2605-2611. AAAI Press, 2013.

Digital Library

Google Scholar

[3]

E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1082-1090. ACM, 2011.

Digital Library

Google Scholar

[4]

D. M. Dunlavy, T. G. Kolda, and E. Acar. Temporal link prediction using matrix and tensor factorizations. ACM Transactions on Knowledge Discovery from Data (TKDD), 5(2):10, 2011.

Digital Library

Google Scholar

[5]

H. Gao and H. Liu. Data analysis on location-based social networks. In Mobile Social Networking, pages 165-194. Springer, 2014.

Crossref

Google Scholar

[6]

L. Hu, A. Sun, and Y. Liu. Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 345-354. ACM, 2014.

Digital Library

Google Scholar

[7]

A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems, pages 79-86. ACM, 2010.

Digital Library

Google Scholar

[8]

T. G. Kolda and B. W. Bader. Tensor decompositions and applications. SIAM review, 51(3):455-500, 2009.

Digital Library

Google Scholar

[9]

H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 287-296. ACM, 2011.

Digital Library

Google Scholar

[10]

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285-295. ACM, 2001.

Digital Library

Google Scholar

[11]

M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pages 325-334. ACM, 2011.

Digital Library

Google Scholar

[12]

Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Time-aware point-of-interest recommendation. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pages 363-372. ACM, 2013.

Digital Library

Google Scholar

[13]

J.-D. Zhang and C.-Y. Chow. igslr: personalized geo-social location recommendation: a kernel density estimation approach. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 334-343. ACM, 2013.

Digital Library

Google Scholar

Cited By

View all

Ahn DSaini UPapalexakis EPayani ASerra ESpezzano F(2024)Neural Additive Tensor Decomposition for Sparse TensorsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679833(14-23)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679833
Gasmi ISoui MBarhoumi KAbed M(2024)Recommendation rules to personalize itineraries for tourists in an unfamiliar cityApplied Soft Computing10.1016/j.asoc.2023.111084150(111084)Online publication date: Jan-2024
https://doi.org/10.1016/j.asoc.2023.111084
Zhou YZhou KChen S(2023)Context-Aware Point-of-Interest Recommendation Based on Similar User Clustering and Tensor FactorizationISPRS International Journal of Geo-Information10.3390/ijgi1204014512:4(145)Online publication date: 29-Mar-2023
https://doi.org/10.3390/ijgi12040145
Show More Cited By

Index Terms

Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization
1. Information systems

Recommendations

A Time-Aware Personalized Point-of-Interest Recommendation via High-Order Tensor Factorization
Special issue: Search, Mining and their Applications on Mobile Devices

Recently, location-based services (LBSs) have been increasingly popular for people to experience new possibilities, for example, personalized point-of-interest (POI) recommendations that leverage on the overlapping of user trajectories to recommend POI ...
Time-aware point-of-interest recommendation
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to ...
Adversarial tensor factorization for context-aware recommendation
RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Contextual factors such as time, location, or tag, can affect user preferences for a particular item. Context-aware recommendations are thus critical to improve both quality and explainability of recommender systems, compared to traditional ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2015

1198 pages

ISBN:9781450336215

DOI:10.1145/2766462

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 August 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

SIGIR '15

Sponsor:

SIGIR

SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

Acceptance Rates

SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

72
Total Citations
View Citations
1,216
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)3

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ahn DSaini UPapalexakis EPayani ASerra ESpezzano F(2024)Neural Additive Tensor Decomposition for Sparse TensorsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679833(14-23)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679833
Gasmi ISoui MBarhoumi KAbed M(2024)Recommendation rules to personalize itineraries for tourists in an unfamiliar cityApplied Soft Computing10.1016/j.asoc.2023.111084150(111084)Online publication date: Jan-2024
https://doi.org/10.1016/j.asoc.2023.111084
Zhou YZhou KChen S(2023)Context-Aware Point-of-Interest Recommendation Based on Similar User Clustering and Tensor FactorizationISPRS International Journal of Geo-Information10.3390/ijgi1204014512:4(145)Online publication date: 29-Mar-2023
https://doi.org/10.3390/ijgi12040145
Ou JJin HWang XJiang HWang XZhou C(2023)STA-TCN: Spatial-temporal Attention over Temporal Convolutional Network for Next Point-of-interest RecommendationACM Transactions on Knowledge Discovery from Data10.1145/359649717:9(1-19)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1145/3596497
Gong WZheng KZhang SHu P(2023)Deep pairwise learning for user preferences via dual graph attention model in location-based social networksExpert Systems with Applications10.1016/j.eswa.2023.120222227(120222)Online publication date: Oct-2023
https://doi.org/10.1016/j.eswa.2023.120222
He YZhou WLuo FGao MWen J(2023)Feature-based POI grouping with transformer for next point of interest recommendationApplied Soft Computing10.1016/j.asoc.2023.110754147:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110754
Abbas RHussain IAmran GAlotaibi SAL-Bakhrani AAlmosharea EAl-qaness M(2023)Trip Recommendation Using Location-Based Social Network: A ReviewInternational Conference on Artificial Intelligence Science and Applications (CAISA)10.1007/978-3-031-28106-8_8(107-119)Online publication date: 3-May-2023
https://doi.org/10.1007/978-3-031-28106-8_8
Rodríguez-Pardo CPatricio MBerlanga AMolina J(2022)Machine Learning for Smart Tourism and RetailResearch Anthology on Machine Learning Techniques, Methods, and Applications10.4018/978-1-6684-6291-1.ch040(753-775)Online publication date: 13-May-2022
https://doi.org/10.4018/978-1-6684-6291-1.ch040
Wang JDeng FWei B(2022)Defect Detection Scheme for Key Equipment of Transmission Line for Complex EnvironmentElectronics10.3390/electronics1115233211:15(2332)Online publication date: 27-Jul-2022
https://doi.org/10.3390/electronics11152332
Dang WWang HPan SZhang PZhou CChen XWang JSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Predicting Human Mobility via Graph Convolutional Dual-attentive NetworksProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498400(192-200)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498400
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

A Time-Aware Personalized Point-of-Interest Recommendation via High-Order Tensor Factorization

Time-aware point-of-interest recommendation

Adversarial tensor factorization for context-aware recommendation