Personalized POI groups recommendation in location-based social networks

Fei Yu, Zhijun Li, Shouxu Jiang, Xiaofei Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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 languageEnglish (US)
Title of host publicationWeb and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings
EditorsChristian S. Jensen, Xiang Lian, Lei Chen, Cyrus Shahabi, Xiaochun Yang
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319635637
StatePublished - 2017
Event1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017 - Beijing, China
Duration: Jul 7 2017Jul 9 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10367 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017

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.


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


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