Personalized POI groups recommendation in location-based social networks

Fei Yu; Zhijun Li; Shouxu Jiang; Xiaofei Yang

doi:10.1007/978-3-319-63564-4_9

Personalized POI groups recommendation in location-based social networks

Fei Yu, Zhijun Li, Shouxu Jiang, Xiaofei Yang

Computer Science and Engineering

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

6 Scopus citations

Abstract

With development of urban modernization, there are a large number of hop spots covering the entire city, defined as Pionts-of-Interest (POIs) Group consist of POIs. POI Groups have a significant impact on people’s lives and urban planning. Every person has her/his own personalized POI Groups (PPGs) based on preferences and friendship in location-based social networks (LBSNs). However, there are almost no researches on this aspect in recommendation systems. This paper proposes a novel PPGs Recommendation algorithm, and models the PPGs by expanding the model of DBSCAN. Our model considers the degree to each PPG covering the target users’ POI preferences. The system recommends the target user with the PPGs which have the top-N largest scores, and it is one NP-hard problem. This paper proposes the greedy algorithm to solve it. Extensive experiments on the two LBSN datasets illustrate the effectiveness of our proposed algorithm.

Original language	English (US)
Title of host publication	Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings
Editors	Christian S. Jensen, Xiang Lian, Lei Chen, Cyrus Shahabi, Xiaochun Yang
Publisher	Springer Verlag
Pages	114-123
Number of pages	10
ISBN (Print)	9783319635637
DOIs	https://doi.org/10.1007/978-3-319-63564-4_9
State	Published - 2017
Event	1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017 - Beijing, China Duration: Jul 7 2017 → Jul 9 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10367 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017
Country/Territory	China
City	Beijing
Period	7/7/17 → 7/9/17

Bibliographical note

Funding Information:
Acknowledgments. This work was supported in part by the National Science Foundation grants NSF-61672196, NSF-61370214, NSF-61300210.

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Density-based clustering
Geo-social distance
POI group recommendation
Personalization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1007/978-3-319-63564-4_9

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Cite this

Yu, F., Li, Z., Jiang, S., & Yang, X. (2017). Personalized POI groups recommendation in location-based social networks. In C. S. Jensen, X. Lian, L. Chen, C. Shahabi, & X. Yang (Eds.), Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings (pp. 114-123). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10367 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-63564-4_9

Personalized POI groups recommendation in location-based social networks. / Yu, Fei; Li, Zhijun; Jiang, Shouxu et al.
Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings. ed. / Christian S. Jensen; Xiang Lian; Lei Chen; Cyrus Shahabi; Xiaochun Yang. Springer Verlag, 2017. p. 114-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10367 LNCS).

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

Yu, F, Li, Z, Jiang, S & Yang, X 2017, Personalized POI groups recommendation in location-based social networks. in CS Jensen, X Lian, L Chen, C Shahabi & X Yang (eds), Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10367 LNCS, Springer Verlag, pp. 114-123, 1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017, Beijing, China, 7/7/17. https://doi.org/10.1007/978-3-319-63564-4_9

Yu F, Li Z, Jiang S, Yang X. Personalized POI groups recommendation in location-based social networks. In Jensen CS, Lian X, Chen L, Shahabi C, Yang X, editors, Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings. Springer Verlag. 2017. p. 114-123. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-63564-4_9

Yu, Fei ; Li, Zhijun ; Jiang, Shouxu et al. / Personalized POI groups recommendation in location-based social networks. Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings. editor / Christian S. Jensen ; Xiang Lian ; Lei Chen ; Cyrus Shahabi ; Xiaochun Yang. Springer Verlag, 2017. pp. 114-123 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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N1 - Funding Information: Acknowledgments. This work was supported in part by the National Science Foundation grants NSF-61672196, NSF-61370214, NSF-61300210. Publisher Copyright: © Springer International Publishing AG 2017.

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AB - With development of urban modernization, there are a large number of hop spots covering the entire city, defined as Pionts-of-Interest (POIs) Group consist of POIs. POI Groups have a significant impact on people’s lives and urban planning. Every person has her/his own personalized POI Groups (PPGs) based on preferences and friendship in location-based social networks (LBSNs). However, there are almost no researches on this aspect in recommendation systems. This paper proposes a novel PPGs Recommendation algorithm, and models the PPGs by expanding the model of DBSCAN. Our model considers the degree to each PPG covering the target users’ POI preferences. The system recommends the target user with the PPGs which have the top-N largest scores, and it is one NP-hard problem. This paper proposes the greedy algorithm to solve it. Extensive experiments on the two LBSN datasets illustrate the effectiveness of our proposed algorithm.

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Personalized POI groups recommendation in location-based social networks

Abstract

Publication series

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Bibliographical note

Keywords

UN SDGs

Publisher link

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