Exploiting social media information toward a context-aware recommendation system

  • Michalis Korakakis
  • Evaggelos Spyrou
  • Phivos Mylonas
  • Stavros J. Perantonis
Original Article


The rise of the social networks during the last few years has provided a vast amount of knowledge in several domains. Among them, route planning and point-of-interest recommendation have significantly benefited. Seen from the side of a tourist, they consist two challenging and time-consuming tasks since they may rely on many parameters and are limited by several constraints, such as time and budget available, user preferences, etc. In this paper we present Xenia, a context-aware system that works toward solving the aforementioned problems. More specifically, it aims to automatically construct travel routes, i.e., ordered visits to various places-of-interest. The user (tourist) indicates an initial and an ending point and her/his available time budget and the system proposes travel routes that maximize her/his travel experience, while adhering to the aforementioned limitations. This particular route planning problem is widely known as the “Tourist Trip Design Problem,” having several variations. In this work we solve this problem by modeling it through the “Orienteering Problem.” We harvest geo-tagged photos from the well-known social network Flickr and using the user-generated textual metadata that accompany them we extract areas-of-interest within a given city along with their underlying semantics. Moreover, by utilizing both the timestamps and the geo-tags of the photos we are able to identify the trajectory patterns of tourists, to detect popular places-of-interest and finally to estimate the average visit duration. Using this historical data we propose travel routes for four of the most popular Greek cities. The effectiveness of our approach is validated upon a twofold validation consisting by (a) a comparison versus the most typical baselines that have adopted by state-of-the-art works and (b) an empirical evaluation by real-life users.


Travel route recommendation AOI extraction socially generated knowledge Flickr 


  1. Arase Y, Xie X, Hara T, Nishio S (2010) Mining people’s trips from large scale geo-tagged photos. In: Proceedings of ACM international conference on multimedia (MM)Google Scholar
  2. Bertram D (2007) Likert scales are the meaning of life. CPSC 681-Topic ReportGoogle Scholar
  3. Brilhante I, Macedo JA, Nardini FM, Perego R, Renso C (2013) Where shall we go today? Planning touristic tours with tripbuilder. In: Proceedings of ACM international conference on information and knowledge managementGoogle Scholar
  4. Campello RJ, Moulavi D, Sander J (2013) Density-based clustering based on hierarchical density estimates. In: Proceedings of the Pacific-Asia conference on knowledge discovery and data miningGoogle Scholar
  5. Cao L, Luo J, Gallagher A, Jin X, Han J, Huang TS (2010) A worldwide tourism recommendation system based on geotagged web photos. In: Proceedings of IEEE ICASSPGoogle Scholar
  6. Chalfen R (1987) Snapshot versions of life. University of Wisconsin Press, MadisonGoogle Scholar
  7. Chao I, Golden B, Wasil E (1996) Theory and methodology—a fast and effective heuristic for the orienteering problem. Eur J Oper Res 88:475–489CrossRefzbMATHGoogle Scholar
  8. Chen YY, Cheng AJ, Hsu WH (2013) Travel recommendation by mining people attributes and travel group types from community-contributed photos. IEEE Trans Multimed 15(6):1283–1295CrossRefGoogle Scholar
  9. De Choudhury M, Feldman M, Amer-Yahia S, Golbandi N, Lempel R, Yu C (2010) Automatic construction of travel itineraries using social breadcrumbs. In: Proceedings of ACM conference on hypertext and hypermediaGoogle Scholar
  10. Duggan M (2016) Photo and video sharing grow online. Retrieved 16 Nov 2016
  11. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96(34):226–231Google Scholar
  12. Gavalas D, Kenteris M (2011) A web-based pervasive recommendation system for mobile tourist guides. Pers Ubiquit Comput 15(7):759–770CrossRefGoogle Scholar
  13. Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) A survey on algorithmic approaches for solving tourist trip design problems. J Heuristics 20(3):291–328CrossRefGoogle Scholar
  14. Gionis A, Lappas T, Pelechrinis K, Terzi E (2014) Customized tour recommendations in urban areas. In: Proceedings of ACM international conference on web search and data mining. ACMGoogle Scholar
  15. Girardin F, Calabrese F, Dal Fiore F, Ratti C, Blat J (2008) Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Comput 7(4):36–43CrossRefGoogle Scholar
  16. Greenwood S, Perrin A, Duggan M (2017) Retrieved 17 Jan 2017
  17. Hao Q, Cai R, Yang J-M, Xiao R, Liu L, Wang S, Zhang L (2009) Travelscope: standing on the shoulders of dedicated travelers. In: Proceedings of ACM MMGoogle Scholar
  18. Hollenstein L, Purves R (2010) Exploring place through user-generated content: using Flickr tags to describe city cores. J Spat Inf Sci 1:21–48Google Scholar
  19. Hsieh HP, Li CT, Lin SD (2014) Measuring and recommending time-sensitive routes from location-based data. ACM Trans Intell Syst Technol (TIST) 5(3):45Google Scholar
  20. Hu Y, Gao S, Janowicz K, Yu B, Li W, Prasad S (2015) Extracting and understanding urban areas of interest using geotagged photos. Comput Environ Urban Syst 54:240–254CrossRefGoogle Scholar
  21. Jain S, Seufert S, Bedathur S (2010) Antourage: mining distance-constrained trips from Flickr. In: Proceedings of ACM WWWGoogle Scholar
  22. Jiang S, Qian X, Shen J, Mei T (2015) Travel recommendation via author topic model based collaborative filtering. Lecture notes in computer science, vol 8936. Springer, Berlin, pp 392–402Google Scholar
  23. Johns R (2010) Likert items and scales. Retrieved 31 Jan 2017
  24. Kisilevich S, Keim D, Andrienko N, Andrienko G (2013) Towards acquisition of semantics of places and events by multi-perspective analysis of geotagged photo collections. In: Moore A, Drecki I (eds) Geospatial visualisation. Springer, Berlin, pp 211–233CrossRefGoogle Scholar
  25. Kurashima T, Iwata T, Irie G, Fujimura K (2010) Travel route recommendation using geotags in photo sharing sites. In: Proceedings of ACM international conference on information and knowledge managementGoogle Scholar
  26. Likert R (1932) A technique for the measurement of attitudes. In: Woodworth RS (ed) Archives of psychology, vol 22, no 140, New York, pp 5–55Google Scholar
  27. Lim KH, Chan J, Leckie C, Karunasekera S (2015) Personalized tour recommendation based on user interests and points of interest visit durations. In: Proceedings of international joint conference on artificial intelligence (IJCAI)Google Scholar
  28. Lim KH, Chan J, Leckie C, Karunasekera S (2016) Towards next generation touring: personalized group tours. In: Proceedings of ICAPSGoogle Scholar
  29. Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Sov Phys Dokl 10:707–10MathSciNetzbMATHGoogle Scholar
  30. Liu Y, Bian J, Agichtein E (2008) Predicting information seeker satisfaction in community question answering. In: Proceedings of international ACM SIGIR conference on research and development in information retrievalGoogle Scholar
  31. Liu J, Huang Z, Chen L, Shen HT, Yan Z (2012) Discovering areas of interest with geo-tagged images and check-ins. In: Proceedings of ACM international conference on multimedia (MM)Google Scholar
  32. Lu X, Wang C, Yang JM, Pang Y, Zhang L (2010) Photo2trip: generating travel routes from geo-tagged photos for trip planning. In: Proceedings of ACM international conference on multimedia (MM)Google Scholar
  33. Lu EHC, Chen CY, Tseng VS (2012) Personalized trip recommendation with multiple constraints by mining user check-in behaviors. In: Proceedings of international conference on advances in GIS. ACMGoogle Scholar
  34. Lu EHC, Fang SH, Tseng VS (2016) Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints. GeoInformatica 20(4):741–763CrossRefGoogle Scholar
  35. Majid A, Chen L, Ling G, Chen HT, Mirza I Hussain, Woodward J (2013) A context-aware personalized travel recommendation system based on geotagged social media data mining. Int J Geogr Inf Sci 27(4):662–684CrossRefGoogle Scholar
  36. Martello S, Toth P (1990) Knapsack problems: algorithms and computer implementations. Wiley, New YorkzbMATHGoogle Scholar
  37. Miller CE, Tucker AW, Zemlin RA (1960) Integer programming formulation of traveling salesman problems. J ACM (JACM) 7(4):326–329MathSciNetCrossRefzbMATHGoogle Scholar
  38. Popescu A, Grefenstette G (2009) Deducing trip related information from flickr. In: Proceedings of ACM WWWGoogle Scholar
  39. Popescu A, Grefenstette G, Moëllic P-A (2009) Mining tourist information from user-supplied collections. In: Proceedings of ACM CIKMGoogle Scholar
  40. Quercia D, Schifanella R, Aiello LM (2014) The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city. In: Proceedings of ACM conference on hypertext and social mediaGoogle Scholar
  41. Schubert E, Koos A, Emrich T, Züfle A, Schmid KA, Zimek A (2015) A framework for clustering uncertain data. Proc VLDB Endow 8(12):1976–1979CrossRefGoogle Scholar
  42. Souffriau W, Vansteenwegen P, Vertommen J, Vanden Berghe G, Van Oudheusden D (2008) A personalized tourist trip design algorithm for mobile tourist guides. Appl Artif Intell 22(10):964–985CrossRefGoogle Scholar
  43. Spyrou E, Mylonas Ph (2016) A survey on Flickr multimedia research challenges. Eng Appl Artif Intell 51:71–91CrossRefGoogle Scholar
  44. Sun Y, Fan H, Bakillah M, Zipf A (2013) Road-based travel recommendation using geo-tagged images. Comput Environ Urban Syst 53:110–122CrossRefGoogle Scholar
  45. Thomee B, Shamma DA, Friedland G, Elizalde B, Ni K, Poland D, Borth D, Li L (2016) YFCC100M: the new data in multimedia research. Commun ACM 59(2):64–73CrossRefGoogle Scholar
  46. Van Canneyt S, Schockaert S, Van Laere O, Dhoedt B (2011) Time-dependent recommendation of tourist attractions using Flickr. In: Proceedings of BNAICGoogle Scholar
  47. Van House NA (2011) Personal photography, digital technologies and the uses of the visual. Vis Stud 26(2):125–134CrossRefGoogle Scholar
  48. Vansteenwegen P, Souffriau W, Berghe GV, Van Oudheusden D (2011a) The city trip planner: an expert system for tourists. Expert Syst Appl 38(6):6540–6546CrossRefGoogle Scholar
  49. Vansteenwegen P, Souffriau W, Van Oudheusden D (2011b) The orienteering problem: a survey. Eur J Oper Res 209(1):1–10MathSciNetCrossRefzbMATHGoogle Scholar
  50. Wu B, Murata Y, Shibata N, Yasumoto K, Ito M (2009) A method for composing tour schedules adaptive to weather change. In: Intelligent vehicles symposium, IEEEGoogle Scholar
  51. Yahi A, Chassang A, Raynaud L, Duthil H, Chau DHP (2015) Aurigo: an interactive tour planner for personalized itineraries. In: Proceedings of international conference on intelligent user interfaces. ACMGoogle Scholar
  52. Yoon H, Zheng Y, Xie X, Woo W (2012) Social itinerary recommendation from user-generated digital trails. Pers Ubiquit Comput 16(5):469–484CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  • Michalis Korakakis
    • 1
  • Evaggelos Spyrou
    • 2
  • Phivos Mylonas
    • 1
  • Stavros J. Perantonis
    • 2
  1. 1.Department of InformaticsIonian UniversityCorfuGreece
  2. 2.Institute of Informatics and TelecommunicationsNational Center for Scientific Research - “Demokritos”AthensGreece

Personalised recommendations