A Holistic Approach to Requirements Elicitation for Mobile Tourist Recommendation Systems

  • Andreas GregoriadesEmail author
  • Maria Pampaka
  • Michael Georgiades
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)


Mobile recommendation systems (MRS) are becoming ever more popular in the tourism industry, due to their potential to declutter the decision-making process of tourists. Despite their proliferation, such systems seem to lack accuracy and relevance to the needs of their users. This paper describes the mobile recommendation problem and explores the relationships between personality, emotion, context and recommendations for tourists. Its aim is to investigate user-requirements of prospective mobile recommendation systems for tourists and the influence of personality and emotional state on user needs. To that end, a survey was conducted with tourists in Cyprus at a point of interest to identify their recommendation needs. Collected data have been analyzed and preliminary results indicate different user requirements among contextual factors. This indicated that the contextualization of these applications in accordance with users’ personality and emotional state is essential to realize their full potential.


Mobile recommendation systems User requirements Personality Emotion Context 


  1. 1.
    Sassi, I.B., Mellouli, S., Yahia, S.B.: Context-aware recommender systems in mobile environment: on the road of future research. Inf. Syst. 72(C), 27–61 (2017)CrossRefGoogle Scholar
  2. 2.
    Sommerville, I., Sawyer, P.: Requirements Engineering: A Good Practice Guide. Wiley, New York (1997)zbMATHGoogle Scholar
  3. 3.
    Yu, E., Giorgini, P., Maiden, N., Mylopoulos, J. (eds.): Social Modeling for Requirements Engineering. MIT Press, Cambridge (2011)Google Scholar
  4. 4.
    Miller, T., Pedell, S., Lopez-Lorca, A.A., Mendoza, A., Sterling, L., Keirman, A.: Emotion-led modelling for people-oriented requirements engineering: the case study of emergency systems. J. Syst. Softw. 105, 54–71 (2015)CrossRefGoogle Scholar
  5. 5.
    Wu, W., Chen, L., He, L.: Using personality to adjust diversity in recommender systems. In: 24th ACM Conference on Hypertext and Social Media (HT 2013), Paris, France, pp. 225–229 (2013)Google Scholar
  6. 6.
    Svendsen, G.B., Johnsen, J.K., Sorensen, L.A., Vitterso, J.: Personality and technology acceptance: the influence of personality factors on the core constructs of the technology acceptance model. Behav. Inf. Technol. 32(4), 323–334 (2013)CrossRefGoogle Scholar
  7. 7.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  8. 8.
    Adomavicius, G., Tuzhilin, A.: 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 (2005)CrossRefGoogle Scholar
  9. 9.
    Buhalis, D.: eTourism: Information Technology for Strategic Tourism Management. Prentice Hall, Upper Saddle River (2003)Google Scholar
  10. 10.
    Kabassi, K.: Personalizing recommendations for tourists. Telematics Inform. 27(1), 51–66 (2010)CrossRefGoogle Scholar
  11. 11.
    Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact. 4(2), 81–173 (2011)CrossRefGoogle Scholar
  12. 12.
    Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.): Content-Based Recommendation. The Adaptive Web, pp. 342–376. Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Interact. 12, 331–370 (2002)zbMATHCrossRefGoogle Scholar
  14. 14.
    Batet, M., Moreno, A., Sánchez, D., Isern, D., Valls, A.: Turist: agent-based personalised recommendation of touristic activities. Expert Syst. Appl. 39(8), 7319–7329 (2012)CrossRefGoogle Scholar
  15. 15.
    Vansteenwegen, P., Souffriau, W., Vanden, G., Van Oudheusden, B.D.: The city trip planner: an expert system for tourists. Expert Syst. Appl. 38(6), 6540–6546 (2010)CrossRefGoogle Scholar
  16. 16.
    Lee, C.S., Chang, Y.C., Wang, M.H.: Ontological recommendation multi-agent for Tainan city travel. Expert Syst. Appl. 36(3), 6740–6753 (2009)CrossRefGoogle Scholar
  17. 17.
    Gavalas, M., Kenteris, A.: web-based pervasive recommendation system for mobile tourist guides. Pers. Ubiquituous Comput. 15(7), 759–770 (2011)CrossRefGoogle Scholar
  18. 18.
    Huang, Y., Bian, L.: A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the internet. Expert Syst. Appl. 36(1), 933–943 (2009)CrossRefGoogle Scholar
  19. 19.
    Lamsfus, C., Alzua-Sorzabal, A., Martin, D., Salvador, Z., Usandizaga, A.: Human-centric ontology-based context modelling in tourism. In: Proceedings of the International Conference on Knowledge Engineering and Ontology Development, Funchal, Madeira, Portugal, pp. 424–434 (2009)Google Scholar
  20. 20.
    Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. User Model. User-Adap. Interact. 17(3), 217–255 (2007)CrossRefGoogle Scholar
  21. 21.
    Hsu, C.K., Hwang, G.J., Chang, C.K.: Development of a reading material recommendation system based on a knowledge engineering approach. Comput. Educ. 55(1), 76–83 (2010)CrossRefGoogle Scholar
  22. 22.
    Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69(1), 99–118 (1955)CrossRefGoogle Scholar
  23. 23.
    Schmoll, G.: Tourism Promotion. Tourism International Press, London (1977)Google Scholar
  24. 24.
    Sirakaya, E., Woodside, A.G.: Building and testing theories of decision-making by travelers. Tour. Manag. 26(6), 815–832 (2005)CrossRefGoogle Scholar
  25. 25.
    Funder, D.C.: Personality Puzzle. W.W. Norton Incorporated, New York (2012)Google Scholar
  26. 26.
    Cuperman, R., Ickes, W.: Big Five predictors of behavior and perceptions in initial dyadic interactions: personality similarity helps extraverts and introverts, but hurts “disagreeables”. J. Pers. Soc. Psychol. 97(4), 667 (2009)CrossRefGoogle Scholar
  27. 27.
    Oliveira, R.D., Cherubini, M., Oliver, N.: Influence of personality on satisfaction with mobile phone services. ACM Trans. Comput.-Hum. Interact. 20(2), 1–23 (2013)CrossRefGoogle Scholar
  28. 28.
    Tupes, E.C., Christal, R.E.: Recurrent personality factors based on trait ratings. J. Pers. 60(2), 225–251 (1992)CrossRefGoogle Scholar
  29. 29.
    Costa Jr, P.T., McCrae, R.: Revised NEO personality inventory (NEO-PI-R) and NEO five factor model (NEO-FFI) professional manual. Psychological Assessment Center, Odessa, FL, USA (1992)Google Scholar
  30. 30.
    Gosling, S.D., Rentfrow, P.J., Swann, W.B.: A very brief measure of the Big-Five personality domains. J. Res. Pers. 37(6), 504–528 (2003)CrossRefGoogle Scholar
  31. 31.
    Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys 2011), Chicago, IL, USA, pp. 197–204 (2011)Google Scholar
  32. 32.
    Tkalcic, M., Kunaver, M., Tasic, J., Košir, A.: Personality based user similarity measure for a collaborative recommender system. In: Proceedings of the Fifth Workshop on Emotion in Human–Computer Interaction-Real World Challenges, Cambridge University, Cambridge, UK, pp. 30–37 (2009)Google Scholar
  33. 33.
    Braunhofer, M., Elahi, M., Ricci, F.: STS: a context-aware mobile ecommender system for places of interest. In: CEUR Workshop Proceedings, Aalborg, Denmark (2014)Google Scholar
  34. 34.
    Braunhofer, M., Ricci, F.: Selective contextual information acquisition in travel recommender systems. Inf. Technol. Tour. 17(5), 5–29 (2017)CrossRefGoogle Scholar
  35. 35.
    Dubé, L., Menon, K.: Multiple roles of consumption emotions in post-purchase satisfaction with extended service transactions. Int. J. Serv. Ind. Manag. 11(3), 287–304 (2000)CrossRefGoogle Scholar
  36. 36.
    Ekman, P.: Facial expressions. In: Handbook of Cognition and Emotion, vol. 16, pp. 301–320 (1999)CrossRefGoogle Scholar
  37. 37.
    Zeelenberg, M., Pieters, R.: Beyond valence in customer dissatisfaction: a review and new findings on behavioral responses to regret and disappointment in failed services. J. Bus. Res. 57(4), 445–455 (2004)CrossRefGoogle Scholar
  38. 38.
    Ortigosa, A., Martín, J.M., Carro, R.M.: Sentiment analysis in Facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014)CrossRefGoogle Scholar
  39. 39.
    Penner, L., Shiffman, S., Paty, J.A., Fritzsche, B.A.: Individual differences in intraperson variability in mood. J. Pers. Soc. Psychol. 66(4), 712 (1994)CrossRefGoogle Scholar
  40. 40.
    Servidio, R., Ruffolo, I.: Exploring the relationship between emotions and memorable tourism experiences through narratives. Tour. Manag. Perspect. 20, 151–160 (2016)CrossRefGoogle Scholar
  41. 41.
    Nawijn, J.: Determinants of daily happiness on vacation. J. Travel Res. 50(5), 559–566 (2011)CrossRefGoogle Scholar
  42. 42.
    Morris, J., Geason, J.: The power of affect: predicting intention. J. Advert. Res. 42(3), 7–17 (2002)CrossRefGoogle Scholar
  43. 43.
    White, C.J., Scandale, S.: The role of emotions in destination visitation intentions: a cross-cultural perspective. J. Hosp. Tour. Manag. 12(2), 168–179 (2005)Google Scholar
  44. 44.
    El Moemen, S.A., Soliman, T.H., Sewisy, A.: A context-aware recommender system for personalized places in mobile applications. Int. J. Adv. Comput. Sci. Appl. 7(3), 442–448 (2016)Google Scholar
  45. 45.
    Siewiorek, D.P., et al.: SenSay: a context-aware mobile phone. In: Seventh IEEE International Symposium on Wearable Computers (2003)Google Scholar
  46. 46.
    Woodside, A., King, R.: An updated model of travel and tourism purchase-consumption systems. J. Travel Tour. Mark. 10, 3–27 (2001)CrossRefGoogle Scholar
  47. 47.
    Pizam, A., Mansfeld, Y.: Consumer Behavior in Travel and Tourism. Haworth Hospitality Press, New York (1999)Google Scholar
  48. 48.
    Decrop, A., Snelders, D.: Planning the summer vacation: an adaptable process. Ann. Tour. Res. 31(4), 1008–1030 (2004)CrossRefGoogle Scholar
  49. 49.
    Jafari, J.: Encyclopedia of Tourism. Routledge, London (2003)Google Scholar
  50. 50.
    Correia, A., Kozak, M., Ferradeira, J.: Impact of culture on tourist decision-making styles. Int. J. Tour. Res. 13, 433–446 (2011)CrossRefGoogle Scholar
  51. 51.
    Becken, S., Wilson, J.: Trip planning and decision-making of self-drive tourists, a quasi-experimental approach. J. Travel Tour. Mark. 20(3/4), 47–62 (2006)Google Scholar
  52. 52.
    Gunn, C.A.: Tourism Planning, 2nd edn. Taylor & Francis, New York (1988)Google Scholar
  53. 53.
    Coughlan, R., Connolly, T.: Predicting affective responses to unexpected outcomes. Organ. Behav. Hum. Decis. Process. 85, 211–225 (2001)CrossRefGoogle Scholar
  54. 54.
    Brusilovsky, P., Kobsa, A., Nejdl, W.: Content-based recommendation systems. In: The Adaptive Web, pp. 325–341. Springer, Heidelberg (2007)Google Scholar
  55. 55.
    Lee, S.A., Shea, L.: Investigating the key routes to customers’ delightful moments in the hotel context. J. Hosp. Mark. Manag. 24(5), 532–553 (2015)Google Scholar
  56. 56.
    Goossens, C.: Tourism information and pleasure motivation. Ann. Tour. Res. 27(2), 301–321 (2000)CrossRefGoogle Scholar
  57. 57.
    Smallman, C., Moore, K.: Process studies of tourists’ decision-making: the riches beyond variance studies. Ann. Tour. Res. 37(2), 397–422 (2010)CrossRefGoogle Scholar
  58. 58.
    Bayardo, R.J.: Efficiently mining long patterns from databases. In: Proceedings of the 1998 ACM-SIGMOD International Conference on Management of Data, Seattle, Washington, USA, pp. 85–93 (1998)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Andreas Gregoriades
    • 1
    Email author
  • Maria Pampaka
    • 2
  • Michael Georgiades
    • 3
  1. 1.Cyprus University of TechnologyLimassolCyprus
  2. 2.The University of ManchesterManchesterUK
  3. 3.Primetel PLCLimassolCyprus

Personalised recommendations