A hybrid method for POI recommendation: combining check-in count, geographical information and reviews

Xiefeng Xu; Pengpeng Zhao; Guanfeng Liu; Caidong Gu; Jiajie Xu; Jian Wu; Zhiming Cui

doi:10.1007/978-3-319-45817-5_13

A hybrid method for POI recommendation: combining check-in count, geographical information and reviews

Xiefeng Xu, Pengpeng Zhao^*, Guanfeng Liu, Caidong Gu, Jiajie Xu, Jian Wu, Zhiming Cui

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

Due to the rapid development of mobile devices, global position systems (GPS),Web 2.0 and location-based social networks (LBSNs) have attracted millions of users to share their locations or experiences. Point of Interest (POI) recommendation plays an important role in exploring attractive locations. POI recommendation is associated with multi-dimensional factors, such as check-in counts, geographical influence, and review text. Although GeoMF can model geographical influence well by matrix factorization (MF), it ignores the impact of review text for POI recommendation. We propose a hybrid method to joint check-in counts, geographical information and reviews for POI recommendation. Specifically, we connect check-in counts and geographical information by incorporating geographical information into matrix factorization. In addition, we combine check-in counts with review text by aligning latent check-in counts in MF, and utilize hidden review topics obtained from LDA by a transformation. The results of our experiments on the real-world dataset show that our proposed model can improve the performance of recommendation.

Original language	English
Title of host publication	Web Technologies and Applications
Subtitle of host publication	18th Asia-Pacific Web Conference, APWeb 2016, Proceedings, Part II
Editors	Feifei Li, Kyuseok Shim, Kai Zheng, Guanfeng Liu
Place of Publication	Switzerland
Publisher	Springer, Springer Nature
Pages	162-173
Number of pages	12
Volume	9932 LNCS
ISBN (Electronic)	9783319458175
ISBN (Print)	9783319458168
DOIs	https://doi.org/10.1007/978-3-319-45817-5_13
Publication status	Published - 2016
Externally published	Yes
Event	Asia-Pacific Web Conference (18th : 2016) - Suzhou, China Duration: 23 Sep 2016 → 25 Sep 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9932 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	Asia-Pacific Web Conference (18th : 2016)
Abbreviated title	APWeb 2016
Country/Territory	China
City	Suzhou
Period	23/09/16 → 25/09/16

Keywords

Geographical information
POI recommendation
Reviews

Access to Document

10.1007/978-3-319-45817-5_13

Cite this

Xu, X., Zhao, P., Liu, G., Gu, C., Xu, J., Wu, J., & Cui, Z. (2016). A hybrid method for POI recommendation: combining check-in count, geographical information and reviews. In F. Li, K. Shim, K. Zheng, & G. Liu (Eds.), Web Technologies and Applications: 18th Asia-Pacific Web Conference, APWeb 2016, Proceedings, Part II (Vol. 9932 LNCS, pp. 162-173). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9932 LNCS). Springer, Springer Nature. https://doi.org/10.1007/978-3-319-45817-5_13

Xu, Xiefeng ; Zhao, Pengpeng ; Liu, Guanfeng ; Gu, Caidong ; Xu, Jiajie ; Wu, Jian ; Cui, Zhiming. / A hybrid method for POI recommendation : combining check-in count, geographical information and reviews. Web Technologies and Applications: 18th Asia-Pacific Web Conference, APWeb 2016, Proceedings, Part II. editor / Feifei Li ; Kyuseok Shim ; Kai Zheng ; Guanfeng Liu. Vol. 9932 LNCS Switzerland : Springer, Springer Nature, 2016. pp. 162-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{44cc95ed9a944c55881c031458675153,

title = "A hybrid method for POI recommendation: combining check-in count, geographical information and reviews",

abstract = "Due to the rapid development of mobile devices, global position systems (GPS),Web 2.0 and location-based social networks (LBSNs) have attracted millions of users to share their locations or experiences. Point of Interest (POI) recommendation plays an important role in exploring attractive locations. POI recommendation is associated with multi-dimensional factors, such as check-in counts, geographical influence, and review text. Although GeoMF can model geographical influence well by matrix factorization (MF), it ignores the impact of review text for POI recommendation. We propose a hybrid method to joint check-in counts, geographical information and reviews for POI recommendation. Specifically, we connect check-in counts and geographical information by incorporating geographical information into matrix factorization. In addition, we combine check-in counts with review text by aligning latent check-in counts in MF, and utilize hidden review topics obtained from LDA by a transformation. The results of our experiments on the real-world dataset show that our proposed model can improve the performance of recommendation.",

keywords = "Geographical information, POI recommendation, Reviews",

author = "Xiefeng Xu and Pengpeng Zhao and Guanfeng Liu and Caidong Gu and Jiajie Xu and Jian Wu and Zhiming Cui",

year = "2016",

doi = "10.1007/978-3-319-45817-5_13",

language = "English",

isbn = "9783319458168",

volume = "9932 LNCS",

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

publisher = "Springer, Springer Nature",

pages = "162--173",

editor = "Feifei Li and Kyuseok Shim and Kai Zheng and Guanfeng Liu",

booktitle = "Web Technologies and Applications",

address = "United States",

note = "Asia-Pacific Web Conference (18th : 2016), APWeb 2016 ; Conference date: 23-09-2016 Through 25-09-2016",

}

Xu, X, Zhao, P, Liu, G, Gu, C, Xu, J, Wu, J & Cui, Z 2016, A hybrid method for POI recommendation: combining check-in count, geographical information and reviews. in F Li, K Shim, K Zheng & G Liu (eds), Web Technologies and Applications: 18th Asia-Pacific Web Conference, APWeb 2016, Proceedings, Part II. vol. 9932 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9932 LNCS, Springer, Springer Nature, Switzerland, pp. 162-173, Asia-Pacific Web Conference (18th : 2016), Suzhou, China, 23/09/16. https://doi.org/10.1007/978-3-319-45817-5_13

A hybrid method for POI recommendation : combining check-in count, geographical information and reviews. / Xu, Xiefeng; Zhao, Pengpeng; Liu, Guanfeng; Gu, Caidong; Xu, Jiajie; Wu, Jian; Cui, Zhiming.

Web Technologies and Applications: 18th Asia-Pacific Web Conference, APWeb 2016, Proceedings, Part II. ed. / Feifei Li; Kyuseok Shim; Kai Zheng; Guanfeng Liu. Vol. 9932 LNCS Switzerland : Springer, Springer Nature, 2016. p. 162-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9932 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - A hybrid method for POI recommendation

T2 - Asia-Pacific Web Conference (18th : 2016)

AU - Xu, Xiefeng

AU - Zhao, Pengpeng

AU - Liu, Guanfeng

AU - Gu, Caidong

AU - Xu, Jiajie

AU - Wu, Jian

AU - Cui, Zhiming

PY - 2016

Y1 - 2016

N2 - Due to the rapid development of mobile devices, global position systems (GPS),Web 2.0 and location-based social networks (LBSNs) have attracted millions of users to share their locations or experiences. Point of Interest (POI) recommendation plays an important role in exploring attractive locations. POI recommendation is associated with multi-dimensional factors, such as check-in counts, geographical influence, and review text. Although GeoMF can model geographical influence well by matrix factorization (MF), it ignores the impact of review text for POI recommendation. We propose a hybrid method to joint check-in counts, geographical information and reviews for POI recommendation. Specifically, we connect check-in counts and geographical information by incorporating geographical information into matrix factorization. In addition, we combine check-in counts with review text by aligning latent check-in counts in MF, and utilize hidden review topics obtained from LDA by a transformation. The results of our experiments on the real-world dataset show that our proposed model can improve the performance of recommendation.

AB - Due to the rapid development of mobile devices, global position systems (GPS),Web 2.0 and location-based social networks (LBSNs) have attracted millions of users to share their locations or experiences. Point of Interest (POI) recommendation plays an important role in exploring attractive locations. POI recommendation is associated with multi-dimensional factors, such as check-in counts, geographical influence, and review text. Although GeoMF can model geographical influence well by matrix factorization (MF), it ignores the impact of review text for POI recommendation. We propose a hybrid method to joint check-in counts, geographical information and reviews for POI recommendation. Specifically, we connect check-in counts and geographical information by incorporating geographical information into matrix factorization. In addition, we combine check-in counts with review text by aligning latent check-in counts in MF, and utilize hidden review topics obtained from LDA by a transformation. The results of our experiments on the real-world dataset show that our proposed model can improve the performance of recommendation.

KW - Geographical information

KW - POI recommendation

KW - Reviews

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U2 - 10.1007/978-3-319-45817-5_13

DO - 10.1007/978-3-319-45817-5_13

M3 - Conference proceeding contribution

AN - SCOPUS:84990068659

SN - 9783319458168

VL - 9932 LNCS

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

SP - 162

EP - 173

BT - Web Technologies and Applications

A2 - Li, Feifei

A2 - Shim, Kyuseok

A2 - Zheng, Kai

A2 - Liu, Guanfeng

PB - Springer, Springer Nature

CY - Switzerland

Y2 - 23 September 2016 through 25 September 2016

ER -

Xu X, Zhao P, Liu G, Gu C, Xu J, Wu J et al. A hybrid method for POI recommendation: combining check-in count, geographical information and reviews. In Li F, Shim K, Zheng K, Liu G, editors, Web Technologies and Applications: 18th Asia-Pacific Web Conference, APWeb 2016, Proceedings, Part II. Vol. 9932 LNCS. Switzerland: Springer, Springer Nature. 2016. p. 162-173. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-45817-5_13

A hybrid method for POI recommendation: combining check-in count, geographical information and reviews

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Keywords

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