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A Holistic Approach to Requirements Elicitation for Mobile Tourist Recommendation Systems

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Advances in Information and Communication (FICC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 69))

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Abstract

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.

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References

  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)

    Article  Google Scholar 

  2. Sommerville, I., Sawyer, P.: Requirements Engineering: A Good Practice Guide. Wiley, New York (1997)

    MATH  Google Scholar 

  3. Yu, E., Giorgini, P., Maiden, N., Mylopoulos, J. (eds.): Social Modeling for Requirements Engineering. MIT Press, Cambridge (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  7. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  9. Buhalis, D.: eTourism: Information Technology for Strategic Tourism Management. Prentice Hall, Upper Saddle River (2003)

    Google Scholar 

  10. Kabassi, K.: Personalizing recommendations for tourists. Telematics Inform. 27(1), 51–66 (2010)

    Article  Google Scholar 

  11. Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact. 4(2), 81–173 (2011)

    Article  Google Scholar 

  12. Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.): Content-Based Recommendation. The Adaptive Web, pp. 342–376. Springer, Heidelberg (2007)

    Google Scholar 

  13. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Interact. 12, 331–370 (2002)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  17. Gavalas, M., Kenteris, A.: web-based pervasive recommendation system for mobile tourist guides. Pers. Ubiquituous Comput. 15(7), 759–770 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  22. Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69(1), 99–118 (1955)

    Article  Google Scholar 

  23. Schmoll, G.: Tourism Promotion. Tourism International Press, London (1977)

    Google Scholar 

  24. Sirakaya, E., Woodside, A.G.: Building and testing theories of decision-making by travelers. Tour. Manag. 26(6), 815–832 (2005)

    Article  Google Scholar 

  25. Funder, D.C.: Personality Puzzle. W.W. Norton Incorporated, New York (2012)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  28. Tupes, E.C., Christal, R.E.: Recurrent personality factors based on trait ratings. J. Pers. 60(2), 225–251 (1992)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. Braunhofer, M., Ricci, F.: Selective contextual information acquisition in travel recommender systems. Inf. Technol. Tour. 17(5), 5–29 (2017)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  36. Ekman, P.: Facial expressions. In: Handbook of Cognition and Emotion, vol. 16, pp. 301–320 (1999)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  40. Servidio, R., Ruffolo, I.: Exploring the relationship between emotions and memorable tourism experiences through narratives. Tour. Manag. Perspect. 20, 151–160 (2016)

    Article  Google Scholar 

  41. Nawijn, J.: Determinants of daily happiness on vacation. J. Travel Res. 50(5), 559–566 (2011)

    Article  Google Scholar 

  42. Morris, J., Geason, J.: The power of affect: predicting intention. J. Advert. Res. 42(3), 7–17 (2002)

    Article  Google Scholar 

  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. 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. Siewiorek, D.P., et al.: SenSay: a context-aware mobile phone. In: Seventh IEEE International Symposium on Wearable Computers (2003)

    Google Scholar 

  46. Woodside, A., King, R.: An updated model of travel and tourism purchase-consumption systems. J. Travel Tour. Mark. 10, 3–27 (2001)

    Article  Google Scholar 

  47. Pizam, A., Mansfeld, Y.: Consumer Behavior in Travel and Tourism. Haworth Hospitality Press, New York (1999)

    Google Scholar 

  48. Decrop, A., Snelders, D.: Planning the summer vacation: an adaptable process. Ann. Tour. Res. 31(4), 1008–1030 (2004)

    Article  Google Scholar 

  49. Jafari, J.: Encyclopedia of Tourism. Routledge, London (2003)

    Google Scholar 

  50. Correia, A., Kozak, M., Ferradeira, J.: Impact of culture on tourist decision-making styles. Int. J. Tour. Res. 13, 433–446 (2011)

    Article  Google Scholar 

  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. Gunn, C.A.: Tourism Planning, 2nd edn. Taylor & Francis, New York (1988)

    Google Scholar 

  53. Coughlan, R., Connolly, T.: Predicting affective responses to unexpected outcomes. Organ. Behav. Hum. Decis. Process. 85, 211–225 (2001)

    Article  Google Scholar 

  54. Brusilovsky, P., Kobsa, A., Nejdl, W.: Content-based recommendation systems. In: The Adaptive Web, pp. 325–341. Springer, Heidelberg (2007)

    Google Scholar 

  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. Goossens, C.: Tourism information and pleasure motivation. Ann. Tour. Res. 27(2), 301–321 (2000)

    Article  Google Scholar 

  57. Smallman, C., Moore, K.: Process studies of tourists’ decision-making: the riches beyond variance studies. Ann. Tour. Res. 37(2), 397–422 (2010)

    Article  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Cyprus University of Technology, Limassol, Cyprus

    Andreas Gregoriades

  2. The University of Manchester, Manchester, UK

    Maria Pampaka

  3. Primetel PLC, Limassol, Cyprus

    Michael Georgiades

Authors
  1. Andreas Gregoriades
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  2. Maria Pampaka
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  3. Michael Georgiades
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Correspondence to Andreas Gregoriades .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Gregoriades, A., Pampaka, M., Georgiades, M. (2020). A Holistic Approach to Requirements Elicitation for Mobile Tourist Recommendation Systems. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-12388-8_58

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