Advertisement

GeoInformatica

, Volume 19, Issue 3, pp 525–565 | Cite as

Recommendations in location-based social networks: a survey

  • Jie Bao
  • Yu Zheng
  • David Wilkie
  • Mohamed Mokbel
Article

Abstract

Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users’ preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users’ travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme.

Keywords

Location-based social networks Recommender systems Location-based services Location recommendations Friend recommendations Community discoveries Activity recommendations Social media recommendations 

References

  1. 1.
    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749CrossRefGoogle Scholar
  2. 2.
    Agrawal R, Srikant R et al (1994) Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB, vol 1215, pp 487–499Google Scholar
  3. 3.
    Arase Y, Xie X, Duan M, Hara T, Nishio S (2009) A game based approach to assign geographical relevance to web images. In: Proceedings of the 18th international conference on World wide web. ACM, pp 811–820Google Scholar
  4. 4.
    Arase Y, Xie X, Hara T, Nishio S (2010) Mining people’s trips from large scale geo-tagged photos. In: Proceedings of the international conference on multimedia. ACM, pp 133–142Google Scholar
  5. 5.
    Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, pp 635–644Google Scholar
  6. 6.
    Backstrom L, Sun E, Marlow C (2010) Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th international conference on World wide web. ACM, pp 61–70Google Scholar
  7. 7.
    Ballatore A, McArdle G, Kelly C, Bertolotto M (2010) Recomap: an interactive and adaptive map-based recommender. In: Proceedings of the 2010 ACM symposium on applied computing. ACM, pp 887–891Google Scholar
  8. 8.
    Bao J, Zheng Y, Mokbel M (2012) Location-based and preference-aware recommendation using sparse geo-social networking data. In: ACM SIGSPATIALGoogle Scholar
  9. 9.
    Borzsony S, Kossmann D, Stocker K (2001) The skyline operator. In: 2001 Proceedings 17th international conference on data engineering. IEEE, pp 421–430Google Scholar
  10. 10.
    Bouidghaghen O, Tamine L, Boughanem M (2011) Personalizing mobile web search for location sensitive queries. In: 2011 12th IEEE international conference on mobile data management (MDM), vol 1. IEEE, pp 110–118Google Scholar
  11. 11.
    Brockmann D, Hufnagel L, Geisel T (2006) The scaling laws of human travel. Nature 439(7075):462–465CrossRefGoogle Scholar
  12. 12.
    Burt RS (1999) The social capital of opinion leaders. Ann Amer Acad Polit Social Sci 566(1):37–54CrossRefGoogle Scholar
  13. 13.
    Cao X, Cong G, Jensen CS (2010) Mining significant semantic locations from gps data. Proc VLDB Endowment 3(1–2):1009–1020CrossRefGoogle Scholar
  14. 14.
    Chakrabarti S, Dom B, Raghavan P, Rajagopalan S, Gibson D, Kleinberg J (1998) Automatic resource compilation by analyzing hyperlink structure and associated text. Comput Netw ISDN Syst 30(1):65–74CrossRefGoogle Scholar
  15. 15.
    Chang K-P, Wei L-Y, Peng W-C, Yeh M-Y (2011) Discovering personalized routes from tejaectories. In: GIS-LBSNGoogle Scholar
  16. 16.
    Chen J, Geyer W, Dugan C, Muller M, Guy I (2009) Make new friends, but keep the old: recommending people on social networking sites. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 201–210Google Scholar
  17. 17.
    Chen Y, Wang W, Liu Z, Lin X (2009) Keyword search on structured and semi-structured data. In: Proceedings of the 2009 ACM SIGMOD international conference on management of data. ACM, pp 1005–1010Google Scholar
  18. 18.
    Cheng Z, Caverlee J, Lee K, Sui DZ (2011) Exploring millions of footprints in location sharing services. ICWSM 2011:81–88Google Scholar
  19. 19.
    Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1082–1090Google Scholar
  20. 20.
    Chow C-Y, Bao J, Mokbel MF (2010) Towards location-based social networking services. In: The 2nd ACM SIGSPATIAL international workshop on location based social networksGoogle Scholar
  21. 21.
    Couldry N, McCarthy A (2004) Mediaspace: place, scale and culture in a media age. RoutledgeGoogle Scholar
  22. 22.
    Cranshaw J, Toch E, Hong J, Kittur A, Sadeh N (2010) Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM international conference on Ubiquitous computing. ACM, pp 119–128Google Scholar
  23. 23.
    Daly EM, Geyer W (2011) Effective event discovery: using location and social information for scoping event recommendations. In: Proceedings of the fifth ACM conference on recommender systems. ACM, pp 277–280Google Scholar
  24. 24.
    Das AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th international conference on World wide web. ACM, pp 271–280Google Scholar
  25. 25.
    Del Prete L (2010) Licia Capra: differs: A mobile recommender service. In: 2010 eleventh international conference on mobile data management (MDM). IEEE, pp 21–26Google Scholar
  26. 26.
    DeScioli P, Kurzban R, Koch EN, Liben-Nowell D (2011) Best friends alliances, friend ranking, and the myspace social network. Perspect Psychol Sci 6(1):6–8CrossRefGoogle Scholar
  27. 27.
    Doyle PG, Laurie Snell J (1984) Random walks and electric networks. Carus Math Monogr:22Google Scholar
  28. 28.
    Doytsher Y, Galon B, Kanza Y (2011) Storing routes in socio-spatial networks and supporting social-based route recommendation. In: Proceedings of the 3nd ACM SIGSPATIAL international workshop on location based social networks. ACMGoogle Scholar
  29. 29.
    Eagle N, Pentland A (2006) Reality mining: sensing complex social systems. Personal Ubiquit Comput 10(4):255–268CrossRefGoogle Scholar
  30. 30.
    Eagle N, Pentland AS (2009) Eigenbehaviors: identifying structure in routine. Behav Ecol Sociobiol 63(7):1057–1066CrossRefGoogle Scholar
  31. 31.
    Eagle N, Pentland AS, Lazer D (2009) Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci 106(36):15274–15278CrossRefGoogle Scholar
  32. 32.
    Ference G, Ye M, Lee W-C (2013) Location recommendation for out-of-town users in location-based social networks. In: Proceedings of the 22nd ACM international conference on conference on information & knowledge management. ACM, pp 721–726Google Scholar
  33. 33.
    Gao H, Tang J, Hu X, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 93–100Google Scholar
  34. 34.
    Gao H, Tang J, Liu H (2014) Addressing the cold-start problem in location recommendation using geo-social correlations. Data Min Knowl Discov:1–25Google Scholar
  35. 35.
    Ge Y, Liu Q, Xiong H, Tuzhilin A, Chen J (2011) Cost-aware travel tour recommendation. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 983–991Google Scholar
  36. 36.
    Ge Y, Xiong H, Tuzhilin A, Xiao K, Gruteser M, Pazzani M (2010) An energy-efficient mobile recommender system. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 899–908Google Scholar
  37. 37.
    Getoor L, Diehl CP (2005) Link mining: a survey. ACM SIGKDD Explor Newslett 7(2):3–12CrossRefGoogle Scholar
  38. 38.
    Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 211–220Google Scholar
  39. 39.
    Goldenberg J, Levy M (2009) Distance is not dead: social interaction and geographical distance in the internet era. arXiv:0906.3202
  40. 40.
    Han J-W, Pei J, Yan X-F (2004) From sequential pattern mining to structured pattern mining: a pattern-growth approach. J Comput Sci Technol 19(3):257–279CrossRefGoogle Scholar
  41. 41.
    Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: ACM SIGMOD record, vol 29–2. ACM, pp 1–12Google Scholar
  42. 42.
    Hao Q, Cai R, Wang C, Xiao R, Yang J-M, Pang Y, Zhang L (2010) Equip tourists with knowledge mined from travelogues. In: Proceedings of the 19th international conference on World wide web. ACM, pp 401–410Google Scholar
  43. 43.
    Harding M, Finney J, Davies N, Rouncefield M, Hannon J (2013) Experiences with a social travel information system. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 173–182Google Scholar
  44. 44.
    Herlocker JL, Konstan JA, Borchers A, Riedl J (1999)Google Scholar
  45. 45.
    Horozov T, Narasimhan N, Vasudevan V (2006) Using location for personalized poi recommendations in mobile environments. In: International symposium on applications and the internet. SAINT 2006. IEEE, 6–ppGoogle Scholar
  46. 46.
    Hu B, Ester M (2013) Spatial topic modeling in online social media for location recommendation. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 25–32Google Scholar
  47. 47.
    Huang L, Li Q, Yue Y (2010) Activity identification from gps trajectories using spatial temporal pois’ attractiveness. In: Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks. ACM, pp 27–30Google Scholar
  48. 48.
    Hung C-C, Chang C-W, Peng W-C (2009) Mining trajectory profiles for discovering user communities. In: Proceedings of the 2009 international workshop on location based social networks. ACM, pp 1–8Google Scholar
  49. 49.
    Jiang B, Yin J, Zhao S (2009) Characterizing the human mobility pattern in a large street network. Phys Rev E 80(2):021136CrossRefGoogle Scholar
  50. 50.
    Kawakubo H, Yanai K (2011) Geovisualrank: a ranking method of geotagged imagesconsidering visual similarity and geo-location proximity. In: Proceedings of the 20th international conference companion on World wide web. ACM, pp 69–70Google Scholar
  51. 51.
    Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM (JACM) 46(5):604–632CrossRefGoogle Scholar
  52. 52.
    Kodama K, Iijima Y, Guo X, Ishikawa Y (2009) Skyline queries based on user locations and preferences for making location-based recommendations. In: Proceedings of the 2009 international workshop on location based social networks. ACM, pp 9–16Google Scholar
  53. 53.
    Lemire D, Maclachlan A (2005) Slope one predictors for online rating-based collaborative filtering. In: SDM, vol 5. SIAM, pp 1–5Google Scholar
  54. 54.
    Leung K W-T, Lee DL, Lee W-C (2011) Clr: a collaborative location recommendation framework based on co-clustering. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 305–314Google Scholar
  55. 55.
    Levandoski J, Sarwat M, Eldawy A, Mokbel M (2012) Lars: a location-aware recommender system. In: IEEE international conference on data engineeringGoogle Scholar
  56. 56.
    Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma W-Y (2008) Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, p 34Google Scholar
  57. 57.
    Li Y, Zhang Z-L, Bao J (2012) Mutual or unrequited love: Identifying stable clusters in social networks with uni-and bi-directional links. In: Algorithms and models for the web graph. Springer, pp 113–125Google Scholar
  58. 58.
    Lian D, Xie X (2011) Learning location naming from user check-in histories. In: ACM SIGSPATIAL. ACMGoogle Scholar
  59. 59.
    Liben-Nowell D, Novak J, Kumar R, Raghavan P, Tomkins A (2005) Geographic routing in social networks. Proc Natl Acad Sci USA 102(33):11623–11628CrossRefGoogle Scholar
  60. 60.
    Lin Y-R, Sun J, Castro P, Konuru R, Sundaram H, Kelliher A (2009) Metafac: community discovery via relational hypergraph factorization. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 527–536Google Scholar
  61. 61.
    Linden G, Smith B, York J (2003) Amazon. com recommendations: item-to-item collaborative filtering. IEEE Int Comput 7(1):76–80CrossRefGoogle Scholar
  62. 62.
    Liu B, Fu Y, Yao Z, Xiong H (2013) Learning geographical preferences for point-of-interest recommendation. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1043–1051Google Scholar
  63. 63.
    Liu H, Wei L-Y, Zheng Y, Schneider M, Peng W-C (2011) Route discovery from mining uncertain trajectories. In: 2011 IEEE 11th international conference on data mining workshops (ICDMW). IEEE, pp 1239–1242Google Scholar
  64. 64.
    Lu C-T, Lei P-R, Peng W-C, Su J (2011) A framework of mining semantic regions from trajectories. In: Database systems for advanced applications. Springer, pp 193–207Google Scholar
  65. 65.
    Lu X, Wang C, Yang J-M, Pang Y, Zhang L (2010) Photo2trip: generating travel routes from geo-tagged photos for trip planning. In: Proceedings of the international conference on multimedia. ACM, pp 143–152Google Scholar
  66. 66.
    Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval, vol 1. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  67. 67.
    Masli M, Bouma L, Owen A, Terveen L (2013) Geowiki+ route analysis= improved transportation planning. In: Proceedings of the 2013 conference on computer supported cooperative work companion. ACM, pp 213–218Google Scholar
  68. 68.
    Mishra N, Schreiber R, Stanton I, Tarjan RE (2007) Clustering social networks. In: Algorithms and models for the web-graph. Springer, pp 56–67Google Scholar
  69. 69.
    Mokbel M, Bao J, Eldawy A, Levandoski J, Sarwat M (2011) Personalization, socialization, and recommendations in location-based services 2.0. In: 5th international VLDB workshop on personalized access, profile management and context awareness in databases (PersDB). VLDBGoogle Scholar
  70. 70.
  71. 71.
    NetFlix Prize Data. http://www.netflixprize.com/
  72. 72.
    Noulas A, Mascolo C, Frias-Martinez E (2013) Exploiting foursquare and cellular data to infer user activity in urban environments. In: 2013 IEEE 14th international conference on mobile data management (MDM), vol 1. IEEE, pp 167–176Google Scholar
  73. 73.
    Noulas A, Scellato S, Mascolo C, Pontil M (2011) An empirical study of geographic user activity patterns in foursquare. ICWSM 11:70–573Google Scholar
  74. 74.
    Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking. Bringing order to the web. Technical ReportGoogle Scholar
  75. 75.
    Panagiotis S, Alexis P, Yannis M, Pinar S, Ismail T (2011) Geo-social recommendations based on incremental tensor reduction and local path traversal. In: Proceedings of the 3nd ACM SIGSPATIAL international workshop on location based social networks. ACMGoogle Scholar
  76. 76.
    Park M-H, Hong J-H, Cho S-B (2007) Location-based recommendation system using bayesian users preference model in mobile devices. In: Ubiquitous intelligence and computing. Springer, pp 1130–1139Google Scholar
  77. 77.
    Alexei P, Christian K (2011) Space-time dynamics of topics in streaming text. In: Proceedings of the 3rd ACM SIGSPATIAL international workshop on location-based social networks. ACM, pp 1–8Google Scholar
  78. 78.
    Quercia D, Lathia N, Calabrese F, Di Lorenzo G, Crowcroft J (2010) Recommending social events from mobile phone location data. In: International conference on data mining. IEEE, pp 971–976Google Scholar
  79. 79.
    Rahimi SM, Wang Xin (2013) Location recommendation based on periodicity of human activities and location categories. In: Advances in knowledge discovery and data mining. Springer, pp 377–389Google Scholar
  80. 80.
    Ramaswamy L, Deepak P, Polavarapu R, Gunasekera K, Garg D, Visweswariah K, Kalyanaraman S (2009) Caesar: a context-aware, social recommender system for low-end mobile devices. In: Tenth international conference on mobile data management: systems, services and middleware, 2009. MDM’09. IEEE, pp 338–347Google Scholar
  81. 81.
    Raymond R, Sugiura T, Tsubouchi K (2011) Location recommendation based on location history and spatio-temporal correlations for an on-demand bus system. In: ACM SIGSPATIAL. ACMGoogle Scholar
  82. 82.
    Roth M, Assaf B-D, Deutscher D, Flysher G, Horn I, Leichtberg A, Leiser N, Matias Y, Merom R (2010) Suggesting friends using the implicit social graph. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 233–242Google Scholar
  83. 83.
    Sandholm T, Ung H (2011) Real-time, location-aware collaborative filtering of web content. In: Proceedings of the 2011 workshop on context-awareness in retrieval and recommendation. ACM, pp 14–18Google Scholar
  84. 84.
    Sarwat M, Eldawy A, Mokbel MF, Riedl J (2013) Plutus: leveraging location-based social networks to recommend potential customers to venues. In: 2013 IEEE 14th international conference on mobile data management (MDM), vol 1. IEEE, pp 26–35Google Scholar
  85. 85.
    Scellato S, Mascolo C, Musolesi M, Crowcroft J (2011) Track globally, deliver locally: improving content delivery networks by tracking geographic social cascades. In: Proceedings of the 20th international conference on World wide web. ACM, pp 457–466Google Scholar
  86. 86.
    Scellato S, Noulas A, Lambiotte R, Mascolo C (2011) Socio-spatial properties of online location-based social networks, vol 11Google Scholar
  87. 87.
    Scellato S, Noulas A, Mascolo C (2011) Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1046–1054Google Scholar
  88. 88.
    Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 253–260Google Scholar
  89. 89.
    Shi Y, Serdyukov P, Hanjalic A, Larson M (2011) Personalized landmark recommendation based on geotags from photo sharing sites. ICWSM 11:622–625Google Scholar
  90. 90.
    Silva A, Martins B (2011) Tag recommendation for georeferenced photos. In: Proceedings of the 3nd ACM SIGSPATIAL international workshop on location based social networks. ACMGoogle Scholar
  91. 91.
    Singh AP, Gordon GJ (2008) Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 650–658Google Scholar
  92. 92.
    Srebro N, Jaakkola T et al (2003) Weighted low-rank approximations. In: ICML, vol 3, pp 720–727Google Scholar
  93. 93.
    Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. SpringerGoogle Scholar
  94. 94.
    Tai CH, Yang D-N, Lin LT, Chen MS (2008) Recommending personalized scenic itinerarywith geo-tagged photos. In: 2008 IEEE international conference on multimedia and expo. IEEE, pp 1209–1212Google Scholar
  95. 95.
    Takeuchi Y, Sugimoto M (2006) Cityvoyager: an outdoor recommendation system based on user location history. In: Ubiquitous intelligence and computing. Springer, pp 625–636Google Scholar
  96. 96.
    Tang KP, Lin J, Hong JI, Siewiorek DP, Sadeh N (2010) Rethinking location sharing: exploring the implications of social-driven vs. purpose-driven location sharing. In: Proceedings of the 12th ACM international conference on ubiquitous computing. ACM, pp 85–94Google Scholar
  97. 97.
    Waldo R (1970) Tobler: a computer movie simulating urban growth in the detroit region. Econ Geogr:234–240Google Scholar
  98. 98.
    Thomas W (1996) Valente: social network thresholds in the diffusion of innovations. Social Netw 18(1):69–89CrossRefGoogle Scholar
  99. 99.
    Venetis P, Gonzalez H, Jensen CS, Halevy A (2011) Hyper-local, directions-based ranking of places. Proc VLDB Endowment 4(5):290–301CrossRefGoogle Scholar
  100. 100.
    Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17 (4):395–416CrossRefGoogle Scholar
  101. 101.
    Weakliam J, Bertolotto M, Wilson D (2005) Implicit interaction profiling for recommending spatial content. In: Proceedings of the 13th annual ACM international workshop on geographic information systems. ACM, pp 285–294Google Scholar
  102. 102.
    Wei L-Y, Zheng Y, Peng W-C (2012) Constructing popular routes from uncertain trajectories. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 195–203Google Scholar
  103. 103.
    Wiese J, Kelley PG, Cranor LF, Dabbish L, Hong JI, Zimmerman J (2011) Are you close with me? are you nearby?: investigating social groups, closeness, and willingness to share. In: Proceedings of the 13th international conference on ubiquitous computing. ACM, pp 197–206Google Scholar
  104. 104.
    Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. In: Proceedings of the 19th international conference on World wide web. ACM, pp 981–990Google Scholar
  105. 105.
    Xiao X, Zheng Y, Luo Q, Xie X (2010) Finding similar users using category-based location history. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 442–445Google Scholar
  106. 106.
    Xiao X, Zheng Y, Luo Q, Xie X (2014) Inferring social ties between users with human location history, vol 5Google Scholar
  107. 107.
    Xu W, Chow C-Y, Zhang J-D (2013) Calba: capacity-aware location-based advertising in temporary social networks. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 354–363Google Scholar
  108. 108.
    Yang D, Zhang D, Yu Z, Yu Z (2013) Fine-grained preference-aware location search leveraging crowdsourced digital footprints from lbsns. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 479–488Google Scholar
  109. 109.
    Ye M, Janowicz K, Mülligann C, Lee W-C (2011) What you are is when you are: the temporal dimension of feature types in location-based social networks. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 102–111Google Scholar
  110. 110.
    Ye M, Shou D, Lee W-C, Yin P, Janowicz K (2011) On the semantic annotation of places in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 520–528Google Scholar
  111. 111.
    Ye M, Yin P, Lee W-C (2010) Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 458–461Google Scholar
  112. 112.
    Ye M, Yin P, Lee W-C, Lee D-L (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 325–334Google Scholar
  113. 113.
    Ye Y, Zheng Y, Chen Y, Feng J, Xie X (2009) Mining individual life pattern based on location history. In: Tenth international conference on mobile data management: systems, services and middleware, 2009. MDM’09. IEEE, pp 1–10Google Scholar
  114. 114.
    Yin H, Sun Y, Cui B, Hu Z, Chen L (2013) Lcars: a location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 221–229Google Scholar
  115. 115.
    Yin Z, Cao L, Han J, Luo J, Huang TS (2011) Diversified trajectory pattern ranking in geo-tagged social media. In: SDM. SIAM, pp 980–991Google Scholar
  116. 116.
    Yin Z, Cao L, Han J, Zhai C, Huang T (2011) Geographical topic discovery and comparison. In: Proceedings of the 20th international conference on World wide web. ACM, pp 247–256Google Scholar
  117. 117.
    Yin Z, Gupta M, Weninger T, Han J (2010) Linkrec: a unified framework for link recommendation with user attributes and graph structure. In: Proceedings of the 19th international conference on World wide web. ACM, pp 1211–1212Google Scholar
  118. 118.
    Ying JJ-C, Lee W-C, Ye M, Chen TC-Y, Tseng VS. (2011) User association analysis of locales on location based social networks. In: GIS-LBSNGoogle Scholar
  119. 119.
    Ying JJ-C, Lu EH-C, Kuo W-N, Tseng VS (2012) Urban point-of-interest recommendation by mining user check-in behaviors. In: Proceedings of the ACM SIGKDD international workshop on urban computing. ACM, pp 63–70Google Scholar
  120. 120.
    Ying JJ-C, Lu EH-C, Lee W-C, Weng T-C, Tseng VS (2010) Mining user similarity from semantic trajectories. In: Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks. ACM, pp 19–26Google Scholar
  121. 121.
    Yoon H, Zheng Y, Xie X, Woo W (2010) Smart itinerary recommendation based on user-generated gps trajectories. In: Ubiquitous intelligence and computing. Springer, pp 19–34Google Scholar
  122. 122.
    Yoon H, Zheng Y, Xie X, Woo W (2012) Social itinerary recommendation from user-generated digital trails. Personal Ubiquit Comput 16(5):469–484CrossRefGoogle Scholar
  123. 123.
    Yu X, Pan A, Tang L-A, Li Z, Han J (2011) Geo-friends recommendation in gps-based cyber-physical social network. In: 2011 International conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 361–368Google Scholar
  124. 124.
    Zhang D, Chee YM, Mondal A, Tung A, Kitsuregawa M (2009) Keyword search in spatial databases: towards searching by document. In: IEEE 25th international conference on data engineering, 2009. ICDE’09. IEEE, pp 688–699Google Scholar
  125. 125.
    Zhang J-D, Chow C-Y (2013) igslr: personalized geo-social location recommendation: a kernel density estimation approach. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 324–333Google Scholar
  126. 126.
    Zheng V, Cao B, Zheng Y, Xie X, Yang Q (2010a) Collaborative filtering meets mobile recommendation: a user-centered approach. In: AAAI conference on artificial intelligenceGoogle Scholar
  127. 127.
    Zheng VW, Zheng Y, Xie X, Yang Q (2010) Collaborative location and activity recommendations with gps history data. In: Proceedings of the 19th international conference on World wide web. ACM, pp 1029–1038Google Scholar
  128. 128.
    Zheng VW, Zheng Y, Xie X, Yang Q (2012) Towards mobile intelligence: learning from gps history data for collaborative recommendation. Artif Intell 184:17–37CrossRefGoogle Scholar
  129. 129.
    Zheng VW, Zheng Y, Yang Q (2009) Joint learning user’s activities and profiles from gps data. In: Proceedings of the 2009 international workshop on location based social networks. ACM, pp 17–20Google Scholar
  130. 130.
    Zheng Y, Capra L, Wolfson O, Yang H (2014) Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol (ACM TIST)Google Scholar
  131. 131.
    Zheng Y, Chen Y, Xie X, Ma W-Y (2009c) GeoLife2.0: a location-based social networking service. In: MDMGoogle Scholar
  132. 132.
    Zheng Y, Xie X (2010) Learning location correlation from gps trajectories. In: 2010 Eleventh international conference on mobile data management (MDM). IEEE, pp 27–32Google Scholar
  133. 133.
    Zheng Y, Xie X (2011) Learning travel recommendations from user-generated gps traces. ACM Trans Intell Sys Technol (TIST) 2(1):2Google Scholar
  134. 134.
    Zheng Y, Zhang L, Ma Z, Xie X, Ma W-Y (2011) Recommending friends and locations based on individual location history. ACM Trans Web (TWEB) 5(1):5Google Scholar
  135. 135.
    Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining correlation between locations using human location history. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 472–475Google Scholar
  136. 136.
    Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th international conference on World wide web. ACM, pp 791–800Google Scholar
  137. 137.
    Zheng Y, Zhou X (2011) Computing with spatial trajectories. SpringerGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Jie Bao
    • 1
  • Yu Zheng
    • 2
  • David Wilkie
    • 3
  • Mohamed Mokbel
    • 1
  1. 1.University of MinnesotaMinneapolisUSA
  2. 2.Microsoft ResearchBeijingChina
  3. 3.University of North CarolinaWilmingtonUSA

Personalised recommendations