Exploiting semantics for context-aware itinerary recommendation

Abstract

Itinerary planning is a challenging task for users wishing to enjoy points of interest (POIs) in line with their preferences, the current context of use, and travel constraints. This article describes an approach to exploit linked open data (LOD) to perform a context-aware recommendation of personalized itineraries with related multimedia content. The recommendation process takes into account the user profile, the context of use, and the characteristics of the POIs extracted from LOD. The system, therefore, consists of six main modules that accomplish the following tasks: (i) the creation of the user profile according to her interests and preferences; (ii) the elicitation of the current context of use; (iii) the extraction and filtering of POIs from LOD through customized and dynamic queries; (iv) the itinerary construction to determine the first K itineraries that match the query; (v) their ranking through a score function that considers several factors, such as the POI popularity, the POI diversity in terms of their categories, the distance and the travel time of the itinerary, the user profile, and her physical and social context; (vi) the recommendation of multimedia and textual contents related to the itinerary suggested to the target user. The results of experimental tests performed on 50 real users show the benefits of the proposed recommender not only in terms of normalized discounted cumulative gain (nDCG), but also in terms of precision and beyond-accuracy metrics.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Notes

  1. 1.

    http://wiki.dbpedia.org/

  2. 2.

    https://www.wikipedia.org/

  3. 3.

    https://www.flickr.com/

  4. 4.

    https://it.foursquare.com/

  5. 5.

    http://linkedgeodata.org

  6. 6.

    https://openweathermap.org/about

  7. 7.

    https://jena.apache.org/

  8. 8.

    https://developer.foursquare.com/

  9. 9.

    https://developers.google.com

References

  1. 1.

    Ricci F, Rokach L, Shapira B (2015) Recommender systems handbook, 2nd edn. Springer Science+Business Media, New York

    Google Scholar 

  2. 2.

    Heitmann B, Hayes C (2010) Using linked data to build open, collaborative recommender systems. In: Linked data meets artificial intelligence, papers from the 2010 AAAI spring symposium, technical report SS-10-07, Stanford, California, USA, March 22-24, 2010, AAAI

  3. 3.

    Di Noia T, Ostuni VC (2015) Recommender systems and linked open data. In: Reasoning Web. Web Logic Rules: 11th international summer school 2015. Springer International Publishing, pp 88–113

  4. 4.

    Gasparetti F (2017) Personalization and context-awareness in social local search: state-of-the-art and future research challenges. Pervasive Mob Comput 38:446–473. https://doi.org/10.1016/j.pmcj.2016.04.004 https://doi.org/10.1016/j.pmcj.2016.04.004. http://www.sciencedirect.com/science/article/pii/S157411921630027X http://www.sciencedirect.com/science/article/pii/S157411921630027X

    Article  Google Scholar 

  5. 5.

    Zhao S, King I, Lyu MR (2016) A survey of point-of-interest recommendation in location-based social networks. CoRR

  6. 6.

    Hyvönen E (2012) Publishing and using cultural heritage linked data on the semantic web, 1st edn. Morgan & Claypool, Palo Alto

    Google Scholar 

  7. 7.

    Ruotsalo T, Haav K, Stoyanov A, Roche S, Fani E, Deliai R, Mäkelä E, Kauppinen T, Hyvönen E (2013) Smartmuseum: a mobile recommender system for the web of data. Web Semant Sci Serv Agents World Wide Web 20:50–67

    Article  Google Scholar 

  8. 8.

    Varfolomeyev A, Korzun D, Ivanovs A, Soms H, Petrina O (2015) Smart space based recommendation service for historical tourism. Procedia Comput Sci 77:85–91. https://doi.org/10.1016/j.procs.2015.12.363 https://doi.org/10.1016/j.procs.2015.12.363. http://www.sciencedirect.com/science/article/pii/S1877050915038739 http://www.sciencedirect.com/science/article/pii/S1877050915038739

    Article  Google Scholar 

  9. 9.

    Lo Bue A, Wecker AJ, Kuflik T, Machì A, Stock O (2015) Providing personalized cultural heritage information for the smart region - a proposed methodology. In: Proceedings of (UMAP 2015), pp 1–7

  10. 10.

    Wang Y, Stash N, Aroyo L, Hollink L, Schreiber G (2009) Semantic relations for content-based recommendations. In: Proceedings of K-CAP ’09. ACM, New York, NY, USA, pp 209–210

  11. 11.

    Di Noia T, Mirizzi R, Ostuni VC, Romito D, Zanker M (2012) Linked open data to support content-based recommender systems. In: Proceedings of the 8th international conference on semantic systems. I-SEMANTICS ’12. ACM, New York, NY, USA, pp 1–8

  12. 12.

    Berners-Lee T (2009) Linked-data design issues W3C design issue document. http://www.w3.org/DesignIssue/LinkedData.html

  13. 13.

    Ostuni VC, Di Noia T, Mirizzi R, Romito D, Di Sciascio E (2012) Cinemappy: a context-aware mobile app for movie recommendations boosted by dbpedia. In: Proceedings of the 2012 international conference on semantic technologies meet recommender systems. SeRSy’12, pp 37–48

  14. 14.

    Staab S, Werthner H, Ricci F, Zipf A, Gretzel U, Fesenmaier DR, Paris C, Knoblock C (2002) Intelligent systems for tourism. IEEE Intell Syst 17(6):53–64

    Article  Google Scholar 

  15. 15.

    Ricci F (2010) Mobile recommender systems. Inf Technol Tourism 12(3):205–231

    Article  Google Scholar 

  16. 16.

    Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32

    Article  Google Scholar 

  17. 17.

    Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333

    Article  Google Scholar 

  18. 18.

    Golden BL, Levy L, Vohra R (1987) The orienteering problem. Nav Res Logist 34(3):307–318. https://doi.org/10.1002/1520-6750(198706)34:3<307::AID-NAV3220340302>3.0.CO;2-D. https://onlinelibrary.wiley.com/doi/abs/10.1002/1520-6750%28198706%2934%3A3%3C307%3A%3AAIDNAV3220340302%3E3.0.CO%3B2-D

    MATH  Article  Google Scholar 

  19. 19.

    Gunawan A, Lau HC, Vansteenwegen P (2016) Orienteering problem: a survey of recent variants, solution approaches and applications. Eur J Oper Res 255:315–332

    MathSciNet  MATH  Article  Google Scholar 

  20. 20.

    Vansteenwegen P, Souffriau W, Oudheusden DV (2011) The orienteering problem: a survey. Eur J Oper Res 209(1):1–10

    MathSciNet  MATH  Article  Google Scholar 

  21. 21.

    Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) A survey on algorithmic approaches for solving tourist trip design problems. J Heuristics 20(3):291–328

    Article  Google Scholar 

  22. 22.

    Souffriau W, Vansteenwegen P, Vertommen J, Berghe GV, Oudheusden DV (2008) A personalized tourist trip design algorithm for mobile tourist guides. Appl Artif Intell 22(10):964– 985

    Article  Google Scholar 

  23. 23.

    Vansteenwegen P, Souffriau W, Vanden Berghe G, Van Oudheusden D (2009) Iterated local search for the team orienteering problem with time windows. Comput Oper Res 36(12):3281– 3290

    MATH  Article  Google Scholar 

  24. 24.

    Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G, Tasoulas Y (2013) Cluster-based heuristics for the team orienteering problem with time windows. In: Bonifaci V, Demetrescu C, Marchetti-Spaccamela A (eds) Experimental algorithms. Springer, Berlin, pp 390–401

  25. 25.

    Likas A, Vlassis NA, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recogn 36 (2):451–461

    Article  Google Scholar 

  26. 26.

    Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou GE, Vathis N, Zaroliagis CD (2015) The eCOMPASS multimodal tourist tour planner. Expert Syst Appl 42(21):7303–7316

    Article  Google Scholar 

  27. 27.

    Lim KH, Chan J, Leckie C, Karunasekera S (2018) Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowl Inf Syst 54(2):375–406

    Article  Google Scholar 

  28. 28.

    Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou GE, Vathis N (2017) Scenic route planning for tourists. Pers Ubiquit Comput 21(1):137–155

    Article  Google Scholar 

  29. 29.

    Vansteenwegen P, Souffriau W, Berghe GV, Oudheusden DV (2011) The city trip planner. Expert Syst Appl 38(6):6540–6546

    Article  Google Scholar 

  30. 30.

    Wörndl W, Hefele A, Herzog D (2017) Recommending a sequence of interesting places for tourist trips. Inf Technol Tourism, pp 1–24

  31. 31.

    Sylejmani K, Dorn J, Musliu N (2017) Planning the trip itinerary for tourist groups. Inf Technol Tourism 17(3):275–314

    Article  Google Scholar 

  32. 32.

    Popescu A, Grefenstette G (2009) Deducing trip related information from Flickr. In: Proceedings of the 18th international conference on world wide web. WWW ’09. ACM, New York, NY, USA, pp 1183–1184

  33. 33.

    Thomee B, Shamma DA, Friedland G, Elizalde B, Ni K, Poland D, Borth D, Li LJ (2016) YFCC100M: The new data in multimedia research. Commun ACM 59(2):64–73

    Article  Google Scholar 

  34. 34.

    Dey AK (2001) Understanding and using context. Pers Ubiquit Comput 5(1):4–7

    Article  Google Scholar 

  35. 35.

    Gao H, Tang J, Liu H (2012) gSCorr: modeling geo-social correlations for new check-ins on location-based social networks. In: Proceedings of CIKM ’12. ACM, New York, NY, USA, pp 1582–1586

  36. 36.

    Zhang T (2004) Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of ICML ’04. ACM, p 116

  37. 37.

    Vrandečić D, Krötzsch M (2014) Wikidata: a free collaborative knowledgebase. Commun ACM 57 (10):78–85. https://doi.acm.org/10.1145/2629489 https://doi.acm.org/10.1145/2629489

    Article  Google Scholar 

  38. 38.

    Passant A (2010) Measuring semantic distance on linking data and using it for resources recommendations. In: AAAI spring symposium: linked data meets artificial intelligence. AAAI Press, pp 93–98

  39. 39.

    Micsik A, Turbucz S, Tóth Z (2015) Exploring publication metadata graphs with the Lodmilla browser and editor. Int J Digital Libraries 16(1):15–24

    Article  Google Scholar 

  40. 40.

    McNee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI ’06 extended abstracts on human factors in computing systems. CHI EA ’06. ACM, New York, NY, USA, pp 1097– 1101

  41. 41.

    Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst 20(4):422–446

    Article  Google Scholar 

  42. 42.

    Onori M, Micarelli A, Sansonetti G (2016) A comparative analysis of personality-based music recommender systems. In: CEUR workshop proceedings. Volume 1680 of CEUR workshop proceedings. Aachen, Germany, CEUR-WS.org, pp 55–59

  43. 43.

    Sansonetti G, Gurini DF, Gasparetti F, Micarelli A (2017) Dynamic social recommendation. In: Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017. ASONAM ’17. ACM, New York, NY, USA, pp 943–947

  44. 44.

    Gurini DF, Gasparetti F, Micarelli A, Sansonetti G (2018) Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Futur Gener Comput Syst 78:430–439. https://doi.org/10.1016/j.future.2017.03.020 https://doi.org/10.1016/j.future.2017.03.020. http://www.sciencedirect.com/science/article/pii/S0167739X17304077 http://www.sciencedirect.com/science/article/pii/S0167739X17304077

    Article  Google Scholar 

  45. 45.

    Musto C, Narducci F, Lops P, De Gemmis M, Semeraro G (2016) Explod: a framework for explaining recommendations based on the linked open data cloud. In: Proceedings of the 10th ACM conference on recommender systems. RecSys ’16. ACM, New York, NY, USA

  46. 46.

    Musto C, Narducci F, Lops P, de Gemmis M, Semeraro G (2018) Linked open data-based explanations for transparent recommender systems. Int J Hum Comput Stud 121:93–107. https://doi.org/10.1016/j.ijhcs.2018.03.003. http://www.sciencedirect.com/science/article/pii/S1071581918300946

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Giuseppe Sansonetti.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fogli, A., Sansonetti, G. Exploiting semantics for context-aware itinerary recommendation. Pers Ubiquit Comput 23, 215–231 (2019). https://doi.org/10.1007/s00779-018-01189-7

Download citation

Keywords

  • Itinerary recommendation
  • Context-awareness
  • Semantics
  • Social networks