Friend recommendation considering preference coverage in location-based social networks

Fei Yu; Nan Che; Zhijun Li; Kai Li; Shouxu Jiang

doi:10.1007/978-3-319-57529-2_8

Friend recommendation considering preference coverage in location-based social networks

Fei Yu^*
, Nan Che
, Zhijun Li
, Kai Li
, Shouxu Jiang

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology
Harbin University of Science and Technology

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

30 Scopus citations

Abstract

Friend recommendation (FR) becomes a valuable service in location-based social networks. Its essential purpose is to meet social demand and demand on obtaining information. The most of current existing friend recommendation methods mainly focus on the preference similarity and common friends between users for improving the recommendation quality. The similar users are likely to have similar preferences of point-of-interests (POIs), the kinds of information they provided are limited and redundant, can not cover all of the target user’s preferences of POIs. This paper aims to improve amount of information on users’ preferences through FR. We give a definition of friend recommendation considering preference coverage problem (FRPCP), and it is also one NP-hard problem. This paper proposes the greedy algorithm to solve the problem. Compared to the existing typical recommendation approaches, the large-scale LBSN datasets validate recommendation quality and significant increase in the degree to preferences coverage.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
Editors	Longbing Cao, Kyuseok Shim, Jae-Gil Lee, Jinho Kim, Yang-Sae Moon, Xuemin Lin
Publisher	Springer Verlag
Pages	91-105
Number of pages	15
ISBN (Print)	9783319575285
DOIs	https://doi.org/10.1007/978-3-319-57529-2_8
State	Published - 2017
Externally published	Yes
Event	21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of Duration: 23 May 2017 → 26 May 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10235 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
Country/Territory	Korea, Republic of
City	Jeju
Period	23/05/17 → 26/05/17

Keywords

Friend recommendation
LBSN
Power-law distribution
Preference coverage

Access to Document

10.1007/978-3-319-57529-2_8

Cite this

Yu, F., Che, N., Li, Z., Li, K., & Jiang, S. (2017). Friend recommendation considering preference coverage in location-based social networks. In L. Cao, K. Shim, J.-G. Lee, J. Kim, Y.-S. Moon, & X. Lin (Eds.), Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings (pp. 91-105). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10235 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-57529-2_8

Yu, Fei ; Che, Nan ; Li, Zhijun et al. / Friend recommendation considering preference coverage in location-based social networks. Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. editor / Longbing Cao ; Kyuseok Shim ; Jae-Gil Lee ; Jinho Kim ; Yang-Sae Moon ; Xuemin Lin. Springer Verlag, 2017. pp. 91-105 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2e6c860d94e5401496eff0156dea3962,

title = "Friend recommendation considering preference coverage in location-based social networks",

abstract = "Friend recommendation (FR) becomes a valuable service in location-based social networks. Its essential purpose is to meet social demand and demand on obtaining information. The most of current existing friend recommendation methods mainly focus on the preference similarity and common friends between users for improving the recommendation quality. The similar users are likely to have similar preferences of point-of-interests (POIs), the kinds of information they provided are limited and redundant, can not cover all of the target user{\textquoteright}s preferences of POIs. This paper aims to improve amount of information on users{\textquoteright} preferences through FR. We give a definition of friend recommendation considering preference coverage problem (FRPCP), and it is also one NP-hard problem. This paper proposes the greedy algorithm to solve the problem. Compared to the existing typical recommendation approaches, the large-scale LBSN datasets validate recommendation quality and significant increase in the degree to preferences coverage.",

keywords = "Friend recommendation, LBSN, Power-law distribution, Preference coverage",

author = "Fei Yu and Nan Che and Zhijun Li and Kai Li and Shouxu Jiang",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 ; Conference date: 23-05-2017 Through 26-05-2017",

year = "2017",

doi = "10.1007/978-3-319-57529-2\_8",

language = "英语",

isbn = "9783319575285",

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

publisher = "Springer Verlag",

pages = "91--105",

editor = "Longbing Cao and Kyuseok Shim and Jae-Gil Lee and Jinho Kim and Yang-Sae Moon and Xuemin Lin",

booktitle = "Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings",

address = "德国",

}

Yu, F, Che, N, Li, Z, Li, K & Jiang, S 2017, Friend recommendation considering preference coverage in location-based social networks. in L Cao, K Shim, J-G Lee, J Kim, Y-S Moon & X Lin (eds), Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10235 LNAI, Springer Verlag, pp. 91-105, 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017, Jeju, Korea, Republic of, 23/05/17. https://doi.org/10.1007/978-3-319-57529-2_8

Friend recommendation considering preference coverage in location-based social networks. / Yu, Fei; Che, Nan; Li, Zhijun et al.
Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. ed. / Longbing Cao; Kyuseok Shim; Jae-Gil Lee; Jinho Kim; Yang-Sae Moon; Xuemin Lin. Springer Verlag, 2017. p. 91-105 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10235 LNAI).

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

TY - GEN

T1 - Friend recommendation considering preference coverage in location-based social networks

AU - Yu, Fei

AU - Che, Nan

AU - Li, Zhijun

AU - Li, Kai

AU - Jiang, Shouxu

PY - 2017

Y1 - 2017

N2 - Friend recommendation (FR) becomes a valuable service in location-based social networks. Its essential purpose is to meet social demand and demand on obtaining information. The most of current existing friend recommendation methods mainly focus on the preference similarity and common friends between users for improving the recommendation quality. The similar users are likely to have similar preferences of point-of-interests (POIs), the kinds of information they provided are limited and redundant, can not cover all of the target user’s preferences of POIs. This paper aims to improve amount of information on users’ preferences through FR. We give a definition of friend recommendation considering preference coverage problem (FRPCP), and it is also one NP-hard problem. This paper proposes the greedy algorithm to solve the problem. Compared to the existing typical recommendation approaches, the large-scale LBSN datasets validate recommendation quality and significant increase in the degree to preferences coverage.

AB - Friend recommendation (FR) becomes a valuable service in location-based social networks. Its essential purpose is to meet social demand and demand on obtaining information. The most of current existing friend recommendation methods mainly focus on the preference similarity and common friends between users for improving the recommendation quality. The similar users are likely to have similar preferences of point-of-interests (POIs), the kinds of information they provided are limited and redundant, can not cover all of the target user’s preferences of POIs. This paper aims to improve amount of information on users’ preferences through FR. We give a definition of friend recommendation considering preference coverage problem (FRPCP), and it is also one NP-hard problem. This paper proposes the greedy algorithm to solve the problem. Compared to the existing typical recommendation approaches, the large-scale LBSN datasets validate recommendation quality and significant increase in the degree to preferences coverage.

KW - Friend recommendation

KW - LBSN

KW - Power-law distribution

KW - Preference coverage

UR - https://www.scopus.com/pages/publications/85018458787

U2 - 10.1007/978-3-319-57529-2_8

DO - 10.1007/978-3-319-57529-2_8

M3 - 会议稿件

AN - SCOPUS:85018458787

SN - 9783319575285

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

SP - 91

EP - 105

BT - Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings

A2 - Cao, Longbing

A2 - Shim, Kyuseok

A2 - Lee, Jae-Gil

A2 - Kim, Jinho

A2 - Moon, Yang-Sae

A2 - Lin, Xuemin

PB - Springer Verlag

T2 - 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017

Y2 - 23 May 2017 through 26 May 2017

ER -

Yu F, Che N, Li Z, Li K, Jiang S. Friend recommendation considering preference coverage in location-based social networks. In Cao L, Shim K, Lee JG, Kim J, Moon YS, Lin X, editors, Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. Springer Verlag. 2017. p. 91-105. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-57529-2_8

Friend recommendation considering preference coverage in location-based social networks

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Keywords

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