Deriving an effective hypergraph model for point of interest recommendation

Meng Qi; Xin Li; Lejian Liao; Dandan Song; Kwok Wai CHEUNG

doi:10.1007/978-3-319-25159-2_71

Deriving an effective hypergraph model for point of interest recommendation

Meng Qi, Xin Li^*, Lejian Liao, Dandan Song, Kwok Wai CHEUNG

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

Point of interest (POI) recommendation on Location Based Social Networks (LBSN) is challenging as the data available for predicting the next point of interest is highly sparse. Addressing the sparsity issue becomes one of the keys to achieve accurate POI recommendation. A promising approach is to explore various types of relevant information carried by the network, e.g, network structures, spatial-temporal information and relations. In this paper, we put forward a hypergraph model to incorporate the higher-order relations of LBSNs for POI recommendation. Accordingly, we propose a hypergraph random walk (HRW) to be applied to such a complex hypergraph. The steady state distribution gives our derived recommendation on venues for each user. Experiments based on a real data set collected from Foursquare have been conducted to evaluate the efficiency and effectiveness of our proposed model with promising results obtained.

Original language	English
Title of host publication	Knowledge Science, Engineering and Management - 8th International Conference, KSEM 2015, Proceedings
Editors	Zili Zhang, Songmao Zhang, Zili Zhang, Martin Wirsing, Martin Wirsing, Martin Wirsing, Zili Zhang, Songmao Zhang, Songmao Zhang
Publisher	Springer Verlag
Pages	771-777
Number of pages	7
ISBN (Print)	9783319251585, 9783319251585, 9783319251585
DOIs	https://doi.org/10.1007/978-3-319-25159-2_71
Publication status	Published - 2015
Event	8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015 - Chongqing, China Duration: 28 Oct 2015 → 30 Oct 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9403
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015
Country/Territory	China
City	Chongqing
Period	28/10/15 → 30/10/15

Scopus Subject Areas

Theoretical Computer Science
General Computer Science

User-Defined Keywords

Hypergraph learning
POI recommendation

Access to Document

10.1007/978-3-319-25159-2_71

Cite this

Qi, M., Li, X., Liao, L., Song, D., & CHEUNG, K. W. (2015). Deriving an effective hypergraph model for point of interest recommendation. In Z. Zhang, S. Zhang, Z. Zhang, M. Wirsing, M. Wirsing, M. Wirsing, Z. Zhang, S. Zhang, & S. Zhang (Eds.), Knowledge Science, Engineering and Management - 8th International Conference, KSEM 2015, Proceedings (pp. 771-777). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9403). Springer Verlag. https://doi.org/10.1007/978-3-319-25159-2_71

Qi, Meng ; Li, Xin ; Liao, Lejian et al. / Deriving an effective hypergraph model for point of interest recommendation. Knowledge Science, Engineering and Management - 8th International Conference, KSEM 2015, Proceedings. editor / Zili Zhang ; Songmao Zhang ; Zili Zhang ; Martin Wirsing ; Martin Wirsing ; Martin Wirsing ; Zili Zhang ; Songmao Zhang ; Songmao Zhang. Springer Verlag, 2015. pp. 771-777 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{85fde564cc5b484cb67666f9b994bd7b,

title = "Deriving an effective hypergraph model for point of interest recommendation",

abstract = "Point of interest (POI) recommendation on Location Based Social Networks (LBSN) is challenging as the data available for predicting the next point of interest is highly sparse. Addressing the sparsity issue becomes one of the keys to achieve accurate POI recommendation. A promising approach is to explore various types of relevant information carried by the network, e.g, network structures, spatial-temporal information and relations. In this paper, we put forward a hypergraph model to incorporate the higher-order relations of LBSNs for POI recommendation. Accordingly, we propose a hypergraph random walk (HRW) to be applied to such a complex hypergraph. The steady state distribution gives our derived recommendation on venues for each user. Experiments based on a real data set collected from Foursquare have been conducted to evaluate the efficiency and effectiveness of our proposed model with promising results obtained.",

keywords = "Hypergraph learning, POI recommendation",

author = "Meng Qi and Xin Li and Lejian Liao and Dandan Song and CHEUNG, {Kwok Wai}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015 ; Conference date: 28-10-2015 Through 30-10-2015",

year = "2015",

doi = "10.1007/978-3-319-25159-2_71",

language = "English",

isbn = "9783319251585",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "771--777",

editor = "Zili Zhang and Songmao Zhang and Zili Zhang and Martin Wirsing and Martin Wirsing and Martin Wirsing and Zili Zhang and Songmao Zhang and Songmao Zhang",

booktitle = "Knowledge Science, Engineering and Management - 8th International Conference, KSEM 2015, Proceedings",

address = "Germany",

}

Qi, M, Li, X, Liao, L, Song, D & CHEUNG, KW 2015, Deriving an effective hypergraph model for point of interest recommendation. in Z Zhang, S Zhang, Z Zhang, M Wirsing, M Wirsing, M Wirsing, Z Zhang, S Zhang & S Zhang (eds), Knowledge Science, Engineering and Management - 8th International Conference, KSEM 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9403, Springer Verlag, pp. 771-777, 8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015, Chongqing, China, 28/10/15. https://doi.org/10.1007/978-3-319-25159-2_71

Deriving an effective hypergraph model for point of interest recommendation. / Qi, Meng; Li, Xin; Liao, Lejian et al.
Knowledge Science, Engineering and Management - 8th International Conference, KSEM 2015, Proceedings. ed. / Zili Zhang; Songmao Zhang; Zili Zhang; Martin Wirsing; Martin Wirsing; Martin Wirsing; Zili Zhang; Songmao Zhang; Songmao Zhang. Springer Verlag, 2015. p. 771-777 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9403).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Deriving an effective hypergraph model for point of interest recommendation

AU - Qi, Meng

AU - Li, Xin

AU - Liao, Lejian

AU - Song, Dandan

AU - CHEUNG, Kwok Wai

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Point of interest (POI) recommendation on Location Based Social Networks (LBSN) is challenging as the data available for predicting the next point of interest is highly sparse. Addressing the sparsity issue becomes one of the keys to achieve accurate POI recommendation. A promising approach is to explore various types of relevant information carried by the network, e.g, network structures, spatial-temporal information and relations. In this paper, we put forward a hypergraph model to incorporate the higher-order relations of LBSNs for POI recommendation. Accordingly, we propose a hypergraph random walk (HRW) to be applied to such a complex hypergraph. The steady state distribution gives our derived recommendation on venues for each user. Experiments based on a real data set collected from Foursquare have been conducted to evaluate the efficiency and effectiveness of our proposed model with promising results obtained.

AB - Point of interest (POI) recommendation on Location Based Social Networks (LBSN) is challenging as the data available for predicting the next point of interest is highly sparse. Addressing the sparsity issue becomes one of the keys to achieve accurate POI recommendation. A promising approach is to explore various types of relevant information carried by the network, e.g, network structures, spatial-temporal information and relations. In this paper, we put forward a hypergraph model to incorporate the higher-order relations of LBSNs for POI recommendation. Accordingly, we propose a hypergraph random walk (HRW) to be applied to such a complex hypergraph. The steady state distribution gives our derived recommendation on venues for each user. Experiments based on a real data set collected from Foursquare have been conducted to evaluate the efficiency and effectiveness of our proposed model with promising results obtained.

KW - Hypergraph learning

KW - POI recommendation

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U2 - 10.1007/978-3-319-25159-2_71

DO - 10.1007/978-3-319-25159-2_71

M3 - Conference proceeding

AN - SCOPUS:84951761353

SN - 9783319251585

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 771

EP - 777

BT - Knowledge Science, Engineering and Management - 8th International Conference, KSEM 2015, Proceedings

A2 - Zhang, Zili

A2 - Zhang, Songmao

A2 - Zhang, Zili

A2 - Wirsing, Martin

A2 - Zhang, Zili

A2 - Zhang, Songmao

PB - Springer Verlag

T2 - 8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015

Y2 - 28 October 2015 through 30 October 2015

ER -

Qi M, Li X, Liao L, Song D, CHEUNG KW. Deriving an effective hypergraph model for point of interest recommendation. In Zhang Z, Zhang S, Zhang Z, Wirsing M, Wirsing M, Wirsing M, Zhang Z, Zhang S, Zhang S, editors, Knowledge Science, Engineering and Management - 8th International Conference, KSEM 2015, Proceedings. Springer Verlag. 2015. p. 771-777. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-25159-2_71

Deriving an effective hypergraph model for point of interest recommendation

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