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What is of interest for tourists in an alpine destination: personalized recommendations for daily activities based on view data

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Abstract

Smartphones are nowadays important tools for tourists. For instance, while on the go in a destination, tourists can use smartphones to find places of interest and to identify activities that might be of interest, as well as a wealth of related information and even special offers in real-time. However, it is time consuming and not easy for tourists in an unknown destination to choose among the numerous options available. Recommendations for instance from other tourists with similar interests would help immensely. Indeed, places and activities are often reviewed and rated by other tourists, however, this information is typically not personalized. This article proposes a recommender system as part of an evolving mobile destination app. Our recommender app is capable of providing personalized recommendations to tourists thereby facilitating and enriching tourists’ experience and stay. This work is based on two qualitative studies towards exploring the information needs of tourists in an alpine destination. These studies were conducted using the mobile ethnography approach and semi-structured interviews. A hybrid recommender system is proposed that uses implicit user feedback in the form of view duration. The proposed system was tested using real data derived from tourists using the mobile app in a Swiss alpine destination. The results of these experiments demonstrate that the system is capable of providing high-quality and diverse recommendations. The core contribution of this work lies in the transformation of the viewing durations to a set of preference values and in learning the optimized weights of the parameters of a hybrid system utilizing an energy minimization framework.

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Notes

  1. Accessed on June 17, 2019: https://www.wttc.org/economic-impact/

References

  • 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):734–749

    Google Scholar 

  • Aslanian E, Radmanesh M, Jalili M (2016) Hybrid recommender systems based on content feature relationship. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2016.2631138

    Article  Google Scholar 

  • Balabanovic M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72

    Google Scholar 

  • Basiri A, Amirian P, Winstanley A, Moore T (2017) Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data. J Ambient Intell Humaniz Comput 9(2):413–427

    Google Scholar 

  • Basu C, Hirsh H, Cohen W (1998) Recommendation as classification: using social and content-based information in recommendation. In: Conference on artificial intelligence. American Association for Artificial Intelligence Menlo Park, CA, 26–30 July 1998, pp 714–720. https://www.aaai.org/Library/IAAI/iaai98contents.php, https://dl.acm.org/citation.cfm?id=295795

  • Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46(7):109–132

    Google Scholar 

  • Borrás J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: a survey. Expert Syst Appl 41(16):7370–7389

    Google Scholar 

  • Buhalis D, Law R (2008) Progress in information technology and tourism management: 20 years on and 10 years after the internet—the state of etourism research. Tour Manag 29(4):609–623

    Google Scholar 

  • Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User Adapt Interact 12(4):331–370

    MATH  Google Scholar 

  • Chen T, Lin C-W (2016) INLP-BPN approach for recommending hotels to a mobile traveler. J Ambient Intell Humaniz Comput 9(2):329–336

    Google Scholar 

  • Chen Y-C, Shang R-A, Kao C-Y (2009) The effects of information overload on consumers’ subjective state towards buying decision in the internet shopping environment. Electron Commer Res Appl 8(1):48–58

    Google Scholar 

  • Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the ACM SIGIR workshop on recommender systems: implementation and evaluation. University of California, Berkeley, 19 Aug 1999. https://www.csee.umbc.edu/~ian/sigir99-rec/#home

  • Ekstrand MD, Riedl JT, Konstan JA (2010) Collaborative filtering recommender systems. Found Trends Hum Comput Interact 4(2):81–173

    Google Scholar 

  • Evans MP (2007) Analysing google rankings through search engine optimization data. Internet Res 17(1):21–37

    Google Scholar 

  • Fesenmaier DR, Xiang Z, Pan B, Law R (2011) A framework of search engine use for travel planning. J Travel Res 50(6):587–601

    Article  Google Scholar 

  • Garcia I, Sebastia L, Onaindia E (2011) On the design of individual and group recommender systems for tourism. Expert Syst Appl 38(6):7683–7692

    Article  Google Scholar 

  • Gavalas D, Kasapakis V, Konstantopoulos C, Mastakas K, Pantziou G (2013) A Survey on Mobile Tourism Recommender Systems. In: International conference on communications and information technology (ICCIT). IEEE, Beirut, 19–21 June 2013, pp 131–135. https://ieeexplore.ieee.org/abstract/document/6579536

  • Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: International conference on research and development in information retrieval. ACM, New York, 15–19 Aug 1999, pp 230–237. https://dl.acm.org/citation.cfm?id=312682

  • Ho C-I, Lin M-H, Chen H-M (2012) Web users’ behavioural patterns of tourism information search: from online to offline. Tour Manag 33(6):1468–1482

    Article  Google Scholar 

  • Jannach D, Zanker M, Felfernig A, Friedrich G (2011) Recommender systems: an introduction. Cambridge University Press, Cambridge

    Google Scholar 

  • Karidi DP, Stavrakas Y, Vassiliou Y (2017) Tweet and followee personalized recommendations based on knowledge graphs. J Ambient Intell Humaniz Comput 9(6):2035–2049

    Google Scholar 

  • Kaššák O, Kompan M, Bieliková M (2016) Personalized hybrid recommendation for group of users: top-N multimedia recommender. Inf Process Manag 52(3):459–477

    Google Scholar 

  • Kenteris M, Gavalas D, Mpitziopoulos A (2010) A mobile tourism recommender system. In: The IEEE symposium on computers and communications. IEEE, Riccione, pp 840–845

  • Lee B-K, Lee W-N (2004) The effect of information overload on consumer choice quality in an on-line environment. Psychol Mark 21(3):159–183

    Google Scholar 

  • Liu Q, Ge Y, Li Z, Chen E, Xiong H (2011) Personalized travel package recommendation. In: International conference on data mining (ICDM). IEEE, Vancouver, 11–14 Dec 2011, pp 407–416. https://ieeexplore.ieee.org/document/6137245

  • Liu Q, Chen E, Xiong H, Ge Y, Li Z, Wu X (2014) A cocktail approach for travel package recommendation. IEEE Trans Knowl Data Eng 26(2):278–293

    Google Scholar 

  • Logesh R, Subramaniyaswamy V (2018) Exploring hybrid recommender systems for personalized travel applications. In: Mallick PK, Balas VE, Bhoi AK, Zobaa AF (eds) Cognitive informatics and soft computing. Springer, Singapore, pp 535–544. https://doi.org/10.1007/978-981-13-0617-4_52

    Chapter  Google Scholar 

  • Lucas JP, Luz N, Moreno MN, Anacleto R, Figueiredo AA, Martins C (2013) A hybrid recommendation approach for a tourism system. Expert Syst Appl 40(9):3532–3550

    Google Scholar 

  • Majeed T, Stämpfli AE, Liebrich A, Meier R (2018) Personalized hybrid recommendations for daily activities in a tourist destination. In: Novais P, Jung JJ, González GV, Fernández-Caballero A, Navarro E, González P, Carneiro D, Pinto A, Campbell AT, Durães D (eds) Advances in intelligent systems and computing. Springer, Cham, pp 155–165

    Google Scholar 

  • Melville P, Mooney RJ, Nagarajan R (2002) Content-boosted collaborative filtering for improved recommend. In: National conference on artificial intelligence. American Association for Artificial Intelligence Menlo Park, Edmonton, 28 July–1 Aug 2002, pp 187–192. https://www.aaai.org/Library/AAAI/aaai02contents.php, https://dl.acm.org/citation.cfm?id=777124

  • Mobasher B, Dai H, Luo T, Nakagawa M (2001) Improving the effectiveness of collaborative filtering on anonymous web usage data. In: International workshop on intelligent techniques for web personalization. Seattle, Aug 2001, pp 53–60

  • Park DH, Kim HK, Choi Y, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072

    Google Scholar 

  • Parsons J, Ralph P (2014) Generating effective recommendations using viewing-time weighted preferences for attributes. J Assoc Inf Syst 15(8):484–513

    Google Scholar 

  • Pazzani MJ (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13(5):393–408

    Google Scholar 

  • Pessemier TD, Dhondt J, Martens L (2016) Hybrid group recommendations for a travel service. Multimed Tools Appl 76(2):2787–2811

    Google Scholar 

  • Petrevska B, Koceski S (2012) Tourism recommendation system: empirical investigation. J Tour 14(4):11–18

    Google Scholar 

  • Plesner A, Clatworthy S (2012) Lessons learned. In: Stickdorn M, Frischhut B (eds) Service design and tourism: case studies of applied research projects on mobile ethnography for tourism destinations. Books on Demand, Norderstedt, pp 110–117

    Google Scholar 

  • Punj G (2012) Consumer decision making on the web: a theoretical analysis and research guidelines. Psychol Mark 29(10):791–803

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: International conference on world wide web. ACM, New York, 1–5 May 2001, pp 285–295. https://dl.acm.org/citation.cfm?id=372071

  • Shaw G, Williams A (2009) Knowledge transfer and management in tourism organisations: an emerging research agenda. Tour Manag 30(3):325–335

    Google Scholar 

  • Smith J, Weeks D, Jacob M, Freeman J, Magerko B (2019) Towards a hybrid recommendation system for a sound library. In: Intelligent user interfaces workshops. ACM, Los Angeles, 16–20 Mar 2019. http://ceur-ws.org/Vol-2327/#MILC

  • Stickdorn M, Frischhut B, Schmid JS (2014) Mobile ethnography: a pioneering research approach for customer-centered destination management. Tour Anal 19(4):491–503

    Google Scholar 

  • Turner R (2019) Travel and tourism economic impact 2019 world. Technical report, World Travel and Tourism Council

  • Verhelä P, Stickdorn M (2012) In search for authentic user insights. In: Stickdorn M, Frischhut B (eds) Service design and tourism: case studies of applied research projects on mobile ethnography for tourism destinations. Books on Demand, Norderstedt, pp 52–63

    Google Scholar 

  • Wang D, Fesenmaier DR (2013) Transforming the travel experience: the use of smartphones for travel. In: Cantoni L, Xiang ZP (eds) Information and communication technologies in tourism. Springer, Berlin, pp 58–69

    Google Scholar 

  • Wang D, Park S, Fesenmaier DR (2011) The role of smartphones in mediating the touristic experience. J Travel Res 51(4):371–387

    Google Scholar 

  • Wang D, Xiang Z, Fesenmaier DR (2014) Adapting to the mobile world: a model of smartphone use. Ann Tour Res 48:11–26

    Google Scholar 

  • Wasfi AMA (1999) Collecting user access patterns for building user profiles and collaborative filtering. In: Proceedings of the 4th international conference on intelligent user interfaces-IUI, ACM, New York, 5–8 Jan 1999, pp 57–64. https://dl.acm.org/citation.cfm?id=291091

  • Werthner H, Ricci F (2004) E-commerce and tourism. Commun ACM 47(12):101–105

    Google Scholar 

  • Yachin JM (2018) The ‘Customer Journey’: learning from customers in tourism experience encounters. Tour Manag Perspect 28:201–210

    Google Scholar 

  • Yang Y, Hooshyar D, Jo J, Lim H (2018) A group preference-based item similarity model: comparison of clustering techniques in ambient and context-aware recommender systems. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-1039-1

    Article  Google Scholar 

  • Zanker M, Jessenitschnig M (2009) Case-studies on exploiting explicit customer requirements in recommender systems. User Model User Adapt Interact 19(1–2):133–166

    Google Scholar 

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Acknowledgements

This work was funded in part by Innosuisse-the Swiss Innovation Agency. The authors would also like to thank ipeak Infosystems for their support and for providing the data that made this work possible.

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Authors and Affiliations

  1. Lucerne University of Applied Sciences and Arts, Suurstoffi 1b, 6343, Rotkreuz, Switzerland

    Tahir Majeed & René Meier

  2. Lucerne University of Applied Sciences and Arts, Rösslimatte 48, 6002, Luzern, Switzerland

    Aline Stämpfli

  3. Lucerne University of Applied Sciences and Arts, Zentralstrasse 9, 6002, Luzern, Switzerland

    Andreas Liebrich

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  1. Tahir Majeed
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  2. Aline Stämpfli
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Correspondence to Tahir Majeed.

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Majeed, T., Stämpfli, A., Liebrich, A. et al. What is of interest for tourists in an alpine destination: personalized recommendations for daily activities based on view data. J Ambient Intell Human Comput 11, 4545–4556 (2020). https://doi.org/10.1007/s12652-019-01619-1

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