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

Computer Science and Engineering

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

29 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 (US)
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
Event	21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of Duration: May 23 2017 → May 26 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

Other

Other	21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
Country/Territory	Korea, Republic of
City	Jeju
Period	5/23/17 → 5/26/17

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

Keywords

Friend recommendation
LBSN
Power-law distribution
Preference coverage

Access

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

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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

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

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, 5/23/17. https://doi.org/10.1007/978-3-319-57529-2_8

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

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)).

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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.

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Friend recommendation considering preference coverage in location-based social networks

Abstract

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Keywords

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