PPS-POI-Rec: a privacy preserving social point-of-interest recommender system

Xiao Liu; An Liu; Guanfeng Liu; Zhixu Li; Jiajie Xu; Pengpeng Zhao; Lei Zhao

doi:10.1007/978-3-319-25255-1_75

PPS-POI-Rec: a privacy preserving social point-of-interest recommender system

Xiao Liu, An Liu, Guanfeng Liu, Zhixu Li, Jiajie Xu, Pengpeng Zhao, Lei Zhao

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

Abstract

Point-of-Interest (POI) recommendation is an important task for location based service (LBS) providers. Social POI recommendation outperforms traditional, non-social approaches as social relations among users (a.k.a. social graph) could be used as a data source to calculate user similarities, which is generally hard to evaluate due to the lack of sufficient user check-in data. However, the social graph is typically owned by a social networking service (SNS) provider such as Facebook and should be hidden from LBS provider for obvious reasons of commercial benefits, as well as due to privacy legislation. In this paper, we present PPS-POI-Rec, a novel privacy preserving social POI recommender system that enables SNS provider and LBS provider to cooperatively recommend a set of POIs to a target user while keeping their private data secret. We will demonstrate step by step how a social POI recommendation can be jointly made by SNS provider and LBS provider, without revealing their private data to each other.

Original language	English
Title of host publication	Web Technologies and Applications
Subtitle of host publication	17th Asia-Pacific Web Conference, APWeb 2015, Proceedings
Editors	Reynold Cheng, Bin Cui, Zhenjie Zhang, Ruichu Cai, Jia Xu
Publisher	Springer, Springer Nature
Pages	875-878
Number of pages	4
ISBN (Electronic)	9783319252551
ISBN (Print)	9783319252544
DOIs	https://doi.org/10.1007/978-3-319-25255-1_75
Publication status	Published - 2015
Externally published	Yes
Event	Asia-Pacific Web Conference (17th : 2015) - Guangzhou, China Duration: 18 Sep 2015 → 20 Sep 2015 Conference number: 17th

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9313
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	Asia-Pacific Web Conference (17th : 2015)
Abbreviated title	APWeb 2015
Country	China
City	Guangzhou
Period	18/09/15 → 20/09/15

Keywords

POI recommendation
Privacy preserving recommendation
Social recommendation

Access to Document

10.1007/978-3-319-25255-1_75

Link to publication in Scopus

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Cite this

Liu, X., Liu, A., Liu, G., Li, Z., Xu, J., Zhao, P., & Zhao, L. (2015). PPS-POI-Rec: a privacy preserving social point-of-interest recommender system. In R. Cheng, B. Cui, Z. Zhang, R. Cai, & J. Xu (Eds.), Web Technologies and Applications: 17th Asia-Pacific Web Conference, APWeb 2015, Proceedings (pp. 875-878). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9313). Springer, Springer Nature. https://doi.org/10.1007/978-3-319-25255-1_75

Liu, Xiao ; Liu, An ; Liu, Guanfeng ; Li, Zhixu ; Xu, Jiajie ; Zhao, Pengpeng ; Zhao, Lei. / PPS-POI-Rec : a privacy preserving social point-of-interest recommender system. Web Technologies and Applications: 17th Asia-Pacific Web Conference, APWeb 2015, Proceedings. editor / Reynold Cheng ; Bin Cui ; Zhenjie Zhang ; Ruichu Cai ; Jia Xu. Springer, Springer Nature, 2015. pp. 875-878 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b338cd7fc11c4db9963ea1c32f9f3f93,

title = "PPS-POI-Rec: a privacy preserving social point-of-interest recommender system",

abstract = "Point-of-Interest (POI) recommendation is an important task for location based service (LBS) providers. Social POI recommendation outperforms traditional, non-social approaches as social relations among users (a.k.a. social graph) could be used as a data source to calculate user similarities, which is generally hard to evaluate due to the lack of sufficient user check-in data. However, the social graph is typically owned by a social networking service (SNS) provider such as Facebook and should be hidden from LBS provider for obvious reasons of commercial benefits, as well as due to privacy legislation. In this paper, we present PPS-POI-Rec, a novel privacy preserving social POI recommender system that enables SNS provider and LBS provider to cooperatively recommend a set of POIs to a target user while keeping their private data secret. We will demonstrate step by step how a social POI recommendation can be jointly made by SNS provider and LBS provider, without revealing their private data to each other.",

keywords = "POI recommendation, Privacy preserving recommendation, Social recommendation",

author = "Xiao Liu and An Liu and Guanfeng Liu and Zhixu Li and Jiajie Xu and Pengpeng Zhao and Lei Zhao",

year = "2015",

doi = "10.1007/978-3-319-25255-1_75",

language = "English",

isbn = "9783319252544",

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

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pages = "875--878",

editor = "Reynold Cheng and Bin Cui and Zhenjie Zhang and Ruichu Cai and Jia Xu",

booktitle = "Web Technologies and Applications",

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note = "Asia-Pacific Web Conference (17th : 2015), APWeb 2015 ; Conference date: 18-09-2015 Through 20-09-2015",

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Liu, X, Liu, A, Liu, G, Li, Z, Xu, J, Zhao, P & Zhao, L 2015, PPS-POI-Rec: a privacy preserving social point-of-interest recommender system. in R Cheng, B Cui, Z Zhang, R Cai & J Xu (eds), Web Technologies and Applications: 17th Asia-Pacific Web Conference, APWeb 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9313, Springer, Springer Nature, pp. 875-878, Asia-Pacific Web Conference (17th : 2015), Guangzhou, China, 18/09/15. https://doi.org/10.1007/978-3-319-25255-1_75

PPS-POI-Rec : a privacy preserving social point-of-interest recommender system. / Liu, Xiao; Liu, An; Liu, Guanfeng; Li, Zhixu; Xu, Jiajie; Zhao, Pengpeng; Zhao, Lei.

Web Technologies and Applications: 17th Asia-Pacific Web Conference, APWeb 2015, Proceedings. ed. / Reynold Cheng; Bin Cui; Zhenjie Zhang; Ruichu Cai; Jia Xu. Springer, Springer Nature, 2015. p. 875-878 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9313).

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

TY - GEN

T1 - PPS-POI-Rec

T2 - Asia-Pacific Web Conference (17th : 2015)

AU - Liu, Xiao

AU - Liu, An

AU - Liu, Guanfeng

AU - Li, Zhixu

AU - Xu, Jiajie

AU - Zhao, Pengpeng

AU - Zhao, Lei

N1 - Conference code: 17th

PY - 2015

Y1 - 2015

N2 - Point-of-Interest (POI) recommendation is an important task for location based service (LBS) providers. Social POI recommendation outperforms traditional, non-social approaches as social relations among users (a.k.a. social graph) could be used as a data source to calculate user similarities, which is generally hard to evaluate due to the lack of sufficient user check-in data. However, the social graph is typically owned by a social networking service (SNS) provider such as Facebook and should be hidden from LBS provider for obvious reasons of commercial benefits, as well as due to privacy legislation. In this paper, we present PPS-POI-Rec, a novel privacy preserving social POI recommender system that enables SNS provider and LBS provider to cooperatively recommend a set of POIs to a target user while keeping their private data secret. We will demonstrate step by step how a social POI recommendation can be jointly made by SNS provider and LBS provider, without revealing their private data to each other.

AB - Point-of-Interest (POI) recommendation is an important task for location based service (LBS) providers. Social POI recommendation outperforms traditional, non-social approaches as social relations among users (a.k.a. social graph) could be used as a data source to calculate user similarities, which is generally hard to evaluate due to the lack of sufficient user check-in data. However, the social graph is typically owned by a social networking service (SNS) provider such as Facebook and should be hidden from LBS provider for obvious reasons of commercial benefits, as well as due to privacy legislation. In this paper, we present PPS-POI-Rec, a novel privacy preserving social POI recommender system that enables SNS provider and LBS provider to cooperatively recommend a set of POIs to a target user while keeping their private data secret. We will demonstrate step by step how a social POI recommendation can be jointly made by SNS provider and LBS provider, without revealing their private data to each other.

KW - POI recommendation

KW - Privacy preserving recommendation

KW - Social recommendation

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DO - 10.1007/978-3-319-25255-1_75

M3 - Conference proceeding contribution

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SN - 9783319252544

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

SP - 875

EP - 878

BT - Web Technologies and Applications

A2 - Cheng, Reynold

A2 - Cui, Bin

A2 - Zhang, Zhenjie

A2 - Cai, Ruichu

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PB - Springer, Springer Nature

Y2 - 18 September 2015 through 20 September 2015

ER -

Liu X, Liu A, Liu G, Li Z, Xu J, Zhao P et al. PPS-POI-Rec: a privacy preserving social point-of-interest recommender system. In Cheng R, Cui B, Zhang Z, Cai R, Xu J, editors, Web Technologies and Applications: 17th Asia-Pacific Web Conference, APWeb 2015, Proceedings. Springer, Springer Nature. 2015. p. 875-878. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-25255-1_75