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LOOKER: a mobile, personalized recommender system in the tourism domain based on social media user-generated content

  • Sondess Missaoui
  • Faten Kassem
  • Marco VivianiEmail author
  • Alessandra Agostini
  • Rim Faiz
  • Gabriella Pasi
Original Article

Abstract

In a ubiquitous computing scenario, characterized by pervasive technologies, tourists can get assistance from mobile technologies in planning their trips. In a context where more and more people own smartphones, tourists expect to get personalized suggestions just in time whenever and wherever they need. To be effective, mobile applications for travel recommendation should consider both the variability of the user’s interests and an effective way to express them while interacting with the environment. This paper presents LOOKER, a mobile recommender system for tourism and travel-related services that considers the above-described issues. It is an adaptable application developed for the Android platform, which takes into account basic contextual information such as location and time, and implements a content-based filtering (CBF) strategy to make personalized suggestions based on the user’s tourism-related user-generated content (UGC) s/he diffuses on social media. Specifically, the CBF strategy implemented in LOOKER is based on a multi-layer user profile, where the layers representing distinct travel-related service categories (e.g., restaurants, hotels, points of interest) are modeled via language models that are defined on the basis of the captured UGC. This allows inferring the interests and the opinions of travelers about the available items. To evaluate the usefulness and the usability of the LOOKER mobile application, user studies have been conducted. The positive outcomes that have been obtained illustrate the potentials of LOOKER.

Keywords

Mobile recommender systems Content-based filtering Personalization Language models User-generated content Social media 

Notes

References

  1. 1.
    Abolfazli S, Sanaei Z, Gani A, Xia F, Yang LT (2014) Rich mobile applications: genesis, taxonomy, and open issues. J Netw Comput Appl 40:345–362CrossRefGoogle Scholar
  2. 2.
    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):734–749CrossRefGoogle Scholar
  3. 3.
    Adomavicius G, Tuzhilin A (2015) Context-aware recommender systems. In: Recommender systems handbook. Springer, pp 191–226Google Scholar
  4. 4.
    Andersen E, Liu YE, Snider R, Szeto R, Popović Z (2011) Placing a value on aesthetics in online casual games. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1275–1278Google Scholar
  5. 5.
    Baltrunas L, et al. (2011) InCarMusic: Context-aware music recommendations in a car. In: Huemer C, Setzer T (eds) E-Commerce and Web technologies. EC-Web 2011. Lecture notes in business information processing, vol 85. Springer, BerlinGoogle Scholar
  6. 6.
    Bangor A, Kortum P, Miller J (2009) Determining what individual sus scores mean: adding an adjective rating scale. J Usability Stud 4(3):114–123Google Scholar
  7. 7.
    Batet M, Moreno A, Sánchez D, Isern D, Valls A (2012) Turist@: agent-based personalised recommendation of tourist activities. Expert Syst Appl 39(8):7319–7329CrossRefGoogle Scholar
  8. 8.
    Belbachir F, Boughanem M, Missen MMS (2014) Probabilistic opinion models based on subjective sources. In: Proceedings of the 29th annual ACM symposium on applied computing. ACM, pp 925–926Google Scholar
  9. 9.
    Bettini C, Brdiczka O, Henricksen K, Indulska J, Nicklas D, Ranganathan A, Riboni D (2010) A survey of context modelling and reasoning techniques. Pervasive Mob Comput 6(2):161–180CrossRefGoogle Scholar
  10. 10.
    Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132CrossRefGoogle Scholar
  11. 11.
    Borras J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: a survey. Expert Syst Appl 41(16):7370–7389CrossRefGoogle Scholar
  12. 12.
    Bouwman H, Carlsson C, Lopez-Nicolas C, Mckenna B, Molina-Castillo F, Tuunanen T, Walden P (2011) Mobile travel services: the effect of moderating context factors. Inform Technol Tourism 13(2):57–74CrossRefGoogle Scholar
  13. 13.
    Braunhofer M, Kaminskas M, Ricci F (2011) Recommending music for places of interest in a mobile travel guide. In: Proceedings of the fifth ACM conference on recommender systems. ACM, pp 253–256Google Scholar
  14. 14.
    Braunhofer M, Ricci F, et al. (2017) Selective contextual information acquisition in travel recommender systems. Inform Technol Tourism 17(1):5–29CrossRefGoogle Scholar
  15. 15.
    Brooke J (2013) SUS: a retrospective. J Usability Stud 8(2):29–40Google Scholar
  16. 16.
    Brooke J, et al (1996) SUS - A quick and dirty usability scale. Usability Eval Industry 189(194):4–7Google Scholar
  17. 17.
    Cantoni L, Saldaña MTL (2016) Mobile systems for tourism. Inform Technol Tourism 16(2):149–151CrossRefGoogle Scholar
  18. 18.
    del Carmen Rodríguez-Hernández M, Ilarri S (2016) Pull-based recommendations in mobile environments. Comput Standards Int 44:185–204CrossRefGoogle Scholar
  19. 19.
    Cenamor I, de la Rosa T, Núñez S, Borrajo D (2017) Planning for tourism routes using social networks. Expert Syst Appl 69:1–9CrossRefGoogle Scholar
  20. 20.
    Clarke CL, Kolla M, Cormack GV, Vechtomova O, Ashkan A, Büttcher S, MacKinnon I (2008) Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval. pp. 659–666. SIGIR ’08. ACM, New YorkGoogle Scholar
  21. 21.
    Colomo-Palacios R, García-peñalvo FJ, Stantchev V, Misra S (2017) Towards a social and context-aware mobile recommendation system for tourism. Pervasive Mobile Comput 38:505–515CrossRefGoogle Scholar
  22. 22.
    Croft WB, Metzler D, Strohman T (2010) Search engines: information retrieval in practice, vol 283. Addison-Wesley, ReadingGoogle Scholar
  23. 23.
    d’Aveni RA, Gunther RE (1995) Hypercompetitive rivalries: competing in highly dynamic environments Free PrGoogle Scholar
  24. 24.
    Davidson J, Liebald B, Liu J, Nandy P, Van Vleet T, Gargi U, Gupta S, He Y, Lambert M, Livingston B et al (2010) The youtube video recommendation system. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp 293–296Google Scholar
  25. 25.
    Dey AK (2001) Understanding and using context. Personal Ubiquitous Comput 5(1):4–7CrossRefGoogle Scholar
  26. 26.
    Felfernig A, Gordea S, Jannach D, Teppan E, Zanker M (2007) A short survey of recommendation technologies in travel and tourism. OEGAI J 25(7):17–22Google Scholar
  27. 27.
    Ferrari E, Viviani M (2013) Privacy in social collaboration. In: Handbook of human computation. Springer, pp 857–878Google Scholar
  28. 28.
    Gavalas D, Kenteris M (2011) A Web-based pervasive recommendation system for mobile tourist guides. Pers Ubiquit Comput 15(7):759–770CrossRefGoogle Scholar
  29. 29.
    Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333CrossRefGoogle Scholar
  30. 30.
    Gunawardana A, Shani G (2015) Evaluating recommender systems. In: Recommender systems handbook. Springer, pp 265–308Google Scholar
  31. 31.
    Hannak A, Sapiezynski P, Molavi Kakhki A, Krishnamurthy B, Lazer D, Mislove A, Wilson C (2013) Measuring personalization of Web search. In: Proceedings of the 22nd international conference on World Wide Web. ACM, pp 527–538Google Scholar
  32. 32.
    Henricksen K, Indulska J (2006) Developing context-aware pervasive computing applications: models and approach. Pervasive Mobile Comput 2(1):37–64CrossRefGoogle Scholar
  33. 33.
    Huang H (2016) Context-aware location recommendation using geotagged photos in social media. ISPRS Int J Geo-Information 5(11):195CrossRefGoogle Scholar
  34. 34.
    Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS) 20(4):422–446CrossRefGoogle Scholar
  35. 35.
    Kennedy-Eden H, Gretzel U (2012) A taxonomy of mobile applications in tourism. E-review Tourism Res 10(2):47–50Google Scholar
  36. 36.
    Kenteris M, Gavalas D, Economou D (2009) An innovative mobile electronic tourist guide application. Personal Ubiquitous Comput 13(2):103–118CrossRefGoogle Scholar
  37. 37.
    Knijnenburg BP, Willemsen MC (2015) Evaluating recommender systems with user experiments. In: Recommender systems handbook. Springer, pp 309–352Google Scholar
  38. 38.
    Kohavi R, Henne RM, Sommerfield D (2007) Practical guide to controlled experiments on the Web: listen to your customers not to the hippo. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 959–967Google Scholar
  39. 39.
    Kullback S, Leibler RA (1951) On information and sufficiency. Annal Math Stat 22(1):79–86MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Levandoski JJ, Sarwat M, Eldawy A, Mokbel MF (2012) Lars: a location-aware recommender system. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE). IEEE, pp 450–461Google Scholar
  41. 41.
    Lewis JR (1995) Ibm computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. Int J Human-Comput Int 7(1):57–78CrossRefGoogle Scholar
  42. 42.
    Loh S, Lorenzi F, Saldaña R, Licthnow D (2003) A tourism recommender system based on collaboration and text analysis. Inform Technol Tourism 6(3):157–165CrossRefGoogle Scholar
  43. 43.
    Lops P, De Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Recommender systems handbook. Springer, pp 73–105Google Scholar
  44. 44.
    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):3532–3550CrossRefGoogle Scholar
  45. 45.
    Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, pp 287–296Google Scholar
  46. 46.
    (2018) Material Design: Onboarding. https://material.io/design/communication/onboarding.html, [Online; accessed 31-Oct-2018]
  47. 47.
    Missaoui S, Viviani M, Faiz R, Pasi G (2017) A language modeling approach for the recommendation of tourism-related services. In: Proceedings of the 32st annual ACM symposium on applied computing. ACM, pp 1075–1076Google Scholar
  48. 48.
    Ning X, Karypis G (2011) Slim: sparse linear methods for top-n recommender systems. In: 11th IEEE International Conference on Data Mining (ICDM). IEEE, pp 497–506Google Scholar
  49. 49.
    Ono C, Takishima Y, Motomura Y, Asoh H (2009) Context-aware preference model based on a study of difference between real and supposed situation data. In: Houben GJ, McCalla G, Pianesi F, Zancanaro M (eds) User modeling, adaptation, and personalization. UMAP 2009. Lecture notes in computer science, vol 5535. Springer, BerlinGoogle Scholar
  50. 50.
    Panniello U, Gorgoglione M (2011) A contextual modeling approach to context-aware recommender systems. In: Proceedings of the 3rd workshop on context-aware recommender systemsGoogle Scholar
  51. 51.
    Panniello U, Tuzhilin A, Gorgoglione M (2014) Comparing context-aware recommender systems in terms of accuracy and diversity. User Model User-Adap Inter 24(1-2):35–65CrossRefGoogle Scholar
  52. 52.
    Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072CrossRefGoogle Scholar
  53. 53.
    Ponte JM, Croft WB (1998) A language modeling approach to information retrieval. In: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 275–281Google Scholar
  54. 54.
    Poslad S, Laamanen H, Malaka R, Nick A, Buckle P, Zipl A (2001) Crumpet: creation of user-friendly mobile services personalised for tourism. In: Proceedings of the second international conference on 3G mobile communication technologies. IETGoogle Scholar
  55. 55.
    Rashid U, Viviani M, Pasi G (2016) A graph-based approach for visualizing and exploring a multimedia search result space. Inf Sci 370:303–322CrossRefGoogle Scholar
  56. 56.
    Ricci F (2010) Mobile recommender systems. Inform Technol Tourism 12(3):205–231CrossRefGoogle Scholar
  57. 57.
    Ricci F, Nguyen QN (2007) Acquiring and revising preferences in a critique-based mobile recommender system. IEEE Intell Syst 22(3):22–29CrossRefGoogle Scholar
  58. 58.
    Savage NS, Baranski M, Chavez NE, Höllerer T (2012) I’m feeling loco: a location based context aware recommendation system. In: Advances in location-based services. Springer, pp 37–54Google Scholar
  59. 59.
    Sebastia L, Garcia I, Onaindia E, Guzman C (2009) e-tourism: a tourist recommendation and planning application. Int J Artif Intell Tool 18(05):717–738CrossRefGoogle Scholar
  60. 60.
    Shani G, Gunawardana A (2011) Evaluating recommendation systems. In: Recommender systems handbook. Springer, pp 257–297Google Scholar
  61. 61.
    Stanciu O, Ṫichindelean M (2010) Consumer behavior in the different sectors of tourism. Stud in Business Econ 5(3):277–285Google Scholar
  62. 62.
    Telfer DJ, Sharpley R (2015) Tourism and development in the developing world RoutledgeGoogle Scholar
  63. 63.
    Tsai CY, Chung SH (2012) A personalized route recommendation service for theme parks using rfid information and tourist behavior. Decis Support Syst 52(2):514–527CrossRefGoogle Scholar
  64. 64.
    Tullis TS, Stetson JN (2004) A comparison of questionnaires for assessing website usability. In: Usability professional association conference, pp 1–12Google Scholar
  65. 65.
    Tumas G, Ricci F (2009) Personalized mobile city transport advisory system. Inform Commun Technol Tourism 2009:173–183Google Scholar
  66. 66.
    Tussyadiah IP, Zach FJ (2012) The role of geo-based technology in place experiences. Ann Tour Res 39(2):780–800CrossRefGoogle Scholar
  67. 67.
    Vansteenwegen P, Souffriau W, Berghe GV, Van Oudheusden D (2011) The city trip planner: an expert system for tourists. Expert Syst Appl 38(6):6540–6546CrossRefGoogle Scholar
  68. 68.
    Villegas NM, Sánchez C, Díaz-cely J, Tamura G (2018) Characterizing context-aware recommender systems: a systematic literature review. Knowl-Based Syst 140:173–200CrossRefGoogle Scholar
  69. 69.
    Viviani M, Pasi G (2017) Credibility in social media: opinions, news, and health information - a survey. Wiley Interdisciplinary Rev: Data Mining Knowl Discovery 7(5):e1209Google Scholar
  70. 70.
    Viviani M, Pasi G (2017) Quantifier guided aggregation for the veracity assessment of online reviews. Int J Intell Syst 32(5):481–501CrossRefGoogle Scholar
  71. 71.
    Wang D, Xiang Z, Fesenmaier DR (2016) Smartphone use in everyday life and travel. J Travel Res 55(1):52–63CrossRefGoogle Scholar
  72. 72.
    Wilson JD, Uminsky DT (2017) The power of A/B testing under interference. arXiv:1710.03855
  73. 73.
    Yang WS, Hwang SY (2013) itravel: a recommender system in mobile peer-to-peer environment. J Syst Softw 86(1):12–20CrossRefGoogle Scholar
  74. 74.
    Younus A, O’Riordan C, Pasi G (2014) A language modeling approach to personalized search based on users’ microblog behavior. In: European conference on information retrieval. Springer, pp 727–732Google Scholar
  75. 75.
    Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 363–372Google Scholar
  76. 76.
    Zhai C, Lafferty J (2004) A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems (TOIS) 22(2):179–214CrossRefGoogle Scholar
  77. 77.
    Zheng Y, Burke R, Mobasher B (2014) Splitting approaches for context-aware recommendation: an empirical study. In: Proceedings of the 29th annual ACM symposium on applied computing. ACM, pp 274–279Google Scholar
  78. 78.
    Zheng Y, Mobasher B, Burke R (2014) Cslim: contextual slim recommendation algorithms. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 301–304Google Scholar
  79. 79.
    Zheng Y, Mobasher B, Burke R (2015) Carskit: a java-based context-aware recommendation engine. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, pp 1668–1671Google Scholar
  80. 80.
    Zhu Q, Wang S, Cheng B, Sun Q, Yang F, Chang RN (2018) Context-aware group recommendation for point-of-interests. IEEE Access 6:12129–12144CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Informatics, Systems, and CommunicationUniversity of Milano-BicoccaMilanItaly
  2. 2.LARODEC, ISGUniversity of TunisLe BardoTunisia
  3. 3.Orange Developer CenterOrangeTunisia
  4. 4.LARODEC, IHECUniversity of CarthageCarthage PresidencyTunisia

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