Skip to main content
SpringerLink
Log in

A Comprehensive Survey on Travel Recommender Systems

  • Original Paper
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Travelling is a combination of journey, transportation, travel-time, accommodation, weather, events, and other aspects which are likely to be experienced by most of the people at some point in their life. To enhance such experience, we generally look for assistance in planning a tour. Today, the information available on tourism-related aspects on the Internet is boundless and exploring suitable travel package/product/service may be time-consuming. A recommender system (RS) can assist for various tour-related queries such as top destinations for summer vacation, preferable climate conditions for tracking, the fastest way to transport, or photography assistance for specific destinations. In this survey, we have presented a pervasive review on travel and associated factors such as hotels, restaurants, tourism package and planning, and attractions; we have also tailored recommendations on a tourist’s diverse requirements such as food, transportation, photography, outfits, safety, and seasonal preferences. We have classified travel-based RSs and presented selection criteria, features, and technical aspects with datasets, methods, and results. We have briefly supplemented research articles from diverse facets; various frameworks for a travel-based RS are discussed. We believe our survey would introduce a state-of-the-art travel RS; it may be utilized to solve the existing limitations and extend its applicability.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Goeldner CR, Ritchie JB (2007) Tourism principles, practices, philosophies. Wiley, New York

    Google Scholar 

  2. Gunn CA et al (1980) Tourism planning. Crane Russak, New York

    Google Scholar 

  3. Ricci F, Rokach L, Shapira B (2015) Recommender systems handbook. Springer, Berlin, pp 1–34

    MATH  Google Scholar 

  4. Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3):56

    Google Scholar 

  5. Burke R (2007) The adaptive web. Springer, Berlin, pp 377–408

    Google Scholar 

  6. 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

    Google Scholar 

  7. Schafer JB, Konstan JA, Riedl J (2001) E-commerce recommendation applications. Data mining and knowledge discovery 5(1–2):115

    MATH  Google Scholar 

  8. Aggarwal CC et al (2016) Recommender systems. Springer, Berlin

    Google Scholar 

  9. Zulkefli NABM, Baharudin BB (2015) Hotel travel recommendation based on blog information. In: International symposium on mathematical sciences and computing research (iSMSC). IEEE, pp 243–248

  10. Utama DN, Lazuardi LI, Qadrya HA, Caroline BM, Renanda T, Sari AP (2017) Worth eat: an intelligent application for restaurant recommendation based on customer preference (case study: five types of restaurant in Tangerang Selatan region, Indonesia). In: 2017 5th international conference on information and communication technology (ICoIC7). IEEE, pp 1–4

  11. Trattner C, Oberegger A, Marinho L, Parra D (2018) Investigating the utility of the weather context for point of interest recommendations. Inf Technol Tour 19(1–4):117

    Google Scholar 

  12. Shelar S, Kamat P, Varpe A, Birajdar A et al (2018) TRAVELMATE travel package recommendation system

  13. Nguyen TN, Ricci F (2018) A chat-based group recommender system for tourism. Inf Technol Tour 18(1–4):5

    Google Scholar 

  14. 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

    Google Scholar 

  15. Gavalas D, Kasapakis V, Konstantopoulos C, Mastakas K, Pantziou G (2013) A survey on mobile tourism recommender systems. In: 2013 third international conference on communications and information technology (ICCIT). IEEE, pp 131–135

  16. Xia F, Asabere NY, Ahmed AM, Li J, Kong X (2013) Mobile multimedia recommendation in smart communities: a survey. IEEE Access 1:606

    Google Scholar 

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

    Google Scholar 

  18. Borràs J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: a survey. Expert Syst Appl 41(16):7370

    Google Scholar 

  19. Anderson C (2018) A survey of food recommenders. arXiv preprint arXiv:1809.02862

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

    Google Scholar 

  21. Sohrabi B, Vanani IR, Tahmasebipur K, Fazli S (2012) An exploratory analysis of hotel selection factors: a comprehensive survey of Tehran hotels. Int J Hosp Manag 31(1):96

    Google Scholar 

  22. Najafi S, Saati S, Bighami M, Abdi F (2013) How do customers evaluate hotel service quality? An empirical study in Tehran hotels. Manag Sci Lett 3(12):3019

    Google Scholar 

  23. Kowisuth P (2015) Factors influence hotel selection of tourists travelling with children in phuket. Ph.D. thesis, Prince of Songkla University

  24. Soulidou AK, Karavasilis GI, Vrana VG, Kehris EC, Theocharidis AIT, Azaria AH (2018) 37. Factors affecting hotel selection. GREEK customers perceptions. TOURMAN 2018:90

    Google Scholar 

  25. Van der Heijden H, Verhagen T, Creemers M (2003) Understanding online purchase intentions: contributions from technology and trust perspectives. Eur J Inf Syst 12(1):41

    Google Scholar 

  26. Agag G, El-Masry AA (2016) Understanding the determinants of hotel booking intentions and moderating role of habit. Int J Hosp Manag 54:52

    Google Scholar 

  27. Lien CH, Wen MJ, Huang LC, Wu KL (2015) Online hotel booking: the effects of brand image, price, trust and value on purchase intentions. Asia Pac Manag Rev 20(4):210

    Google Scholar 

  28. Boon-Itt S, Rompho N (2012) Measuring service quality dimensions: an empirical analysis of Thai hotel industry. Int J Bus Adm 3(5):52

    Google Scholar 

  29. Zaman M, Botti L, Thanh TV (2016) Weight of criteria in hotel selection: an empirical illustration based on TripAdvisor criteria. Eur J Tour Res 13:132

    Google Scholar 

  30. Sanchez J, Callarisa L, Rodriguez RM, Moliner MA (2006) Perceived value of the purchase of a tourism product. Tour Manag 27(3):394

    Google Scholar 

  31. Gardial SF, Clemons DS, Woodruff RB, Schumann DW, Burns MJ (1994) Comparing consumers’ recall of prepurchase and postpurchase product evaluation experiences. J Consum Res 20(4):548

    Google Scholar 

  32. Sweeney JC, Soutar GN (2001) Consumer perceived value: the development of a multiple item scale. J Retail 77(2):203

    Google Scholar 

  33. Bilgihan A (2016) Gen Y customer loyalty in online shopping: an integrated model of trust, user experience and branding. Comput Hum Behav 61:103

    Google Scholar 

  34. Saga R, Hayashi Y, Tsuji H (2008) Hotel recommender system based on user’s preference transition. In: IEEE International conference on systems, man and cybernetics, 2008. SMC 2008. IEEE, pp 2437–2442

  35. Nilashi M, bin Ibrahim O, Ithnin N, Sarmin NH (2015) A multi-criteria collaborative filtering recommender system for the tourism domain using expectation maximization (EM) and PCA–ANFIS. Electron Commer Res Appl 14(6):542

    Google Scholar 

  36. Jannach D, Karakaya Z, Gedikli F (2012) Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 13th ACM conference on electronic commerce. ACM, pp 674–689

  37. Sen S, Vig J, Riedl J (2009) Tagommenders: connecting users to items through tags. In: Proceedings of the 18th international conference on World Wide Web. ACM, pp 671–680

  38. Herlocker J, Konstan JA, Riedl J (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retr 5(4):287

    Google Scholar 

  39. Zhang Z, Morimoto Y (2017) Collaborative hotel recommendation based on topic and sentiment of review comments. In: proceeding of the 9th forum for information and engineering, DEIM

  40. O’Mahony MP, Smyth B (2009) Learning to recommend helpful hotel reviews. In: Proceedings of the third ACM conference on recommender systems. ACM, pp 305–308

  41. Srisuan J, Hanskunatai A (2014) The ensemble of Naïve Bayes classifiers for hotel searching. In: 2014 International computer science and engineering conference (ICSEC). IEEE, pp 168–173

  42. Levi A, Mokryn O, Diot C, Taft N (2012) Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. In: Proceedings of the sixth ACM conference on recommender systems. ACM, pp 115–122

  43. Hayes C, Cunningham P (2002) An on-line evaluation framework forrecommender systems. Technical report, Trinity College Dublin, Department of Computer Science

  44. Lin KP, Lai CY, Chen PC, Hwang SY (2015) Personalized hotel recommendation using text mining and mobile browsing tracking. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 191–196

  45. Cruz FL, Troyano JA, Enríquez F, Ortega FJ, Vallejo CG (2013) Long autonomy or long delay? The importance of domain in opinion mining. Expert Syst Appl 40(8):3174

    Google Scholar 

  46. Fiz Pontiveros BB (2012) Opinion mining from a large corpora of natural language reviews. Master’s thesis, Universitat Politècnica de Catalunya

  47. Al-Ghossein M, Abdessalem T, Barré A (2018) Cross-domain recommendation in the hotel sector. In: RecTour 2018

  48. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, pp 452–461

  49. Thomee B, Shamma DA, Friedland G, Elizalde B, Ni K, Poland D, Borth D, Li LJ (2015) YFCC100M: the new data in multimedia research. arXiv preprint arXiv:1503.01817

  50. Walek B, Hosek O, Farana R (2016) Proposal of expert system for hotel booking system. In: 2016 17th international carpathian control conference (ICCC). IEEE, pp 804–807

  51. Pokorny M (1996) Artificial Intelligence in modelling and control. BEN-technická literatura, Praha, p 189 (in Czech)

    Google Scholar 

  52. Habiballa H, Novák V, Dvořák A, Pavliska V (2003) Using software package LFLC 2000. In: 2nd international conference aplimat, pp 355–358

  53. Chu WT, Huang WH (2017) Cultural difference and visual information on hotel rating prediction. World Wide Web 20(4):595

    Google Scholar 

  54. Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 783–792

  55. Wang H, Lu Y, Zhai C (2011) Latent aspect rating analysis without aspect keyword supervision. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 618–626

  56. Nam JH, Lee TJ (2011) Foreign travelers’ satisfaction with traditional Korean restaurants. Int J Hosp Manag 30(4):982

    Google Scholar 

  57. Koku PS, Savas S (2016) Restaurant tipping and customers’ susceptibility to emotional contagion. J Serv Mark 30(7):762

    Google Scholar 

  58. Alhelalat JA, Ma’moun AH, Twaissi NM (2017) The impact of personal and functional aspects of restaurant employee service behaviour on customer satisfaction. Int J Hosp Manag 66:46

    Google Scholar 

  59. Ponnam A, Balaji M (2014) Matching visitation-motives and restaurant attributes in casual dining restaurants. Int J Hosp Manag 37:47

    Google Scholar 

  60. Ariffin HF, Bibon MF, Abdullah RPSR (2017) Restaurant’s atmospheric elements: what the customer wants. J Asian Behav Stud 2(3):85

    Google Scholar 

  61. Thakur R, Hale D, Summey JH (2018) What motivates consumers to partake in cyber shilling? J Mark Theory Pract 26(1–2):181

    Google Scholar 

  62. Tham A, Wang M (2017) There’sa troll on the information bridge! An exploratory study of deviant online behaviour impacts on tourism cosmopolitanism. Tour Recreat Res 42(2):258

    Google Scholar 

  63. Rhee HT, Yang SB, Kim K (2016) Exploring the comparative salience of restaurant attributes: a conjoint analysis approach. Int J Inf Manag 36(6):1360

    Google Scholar 

  64. Wen-ying Z, Guo-ming Q (2013) A new framework of a personalized location-based restaurant recommendation system in mobile application. In: 2013 international conference on management science and engineering (ICMSE). IEEE, pp 166–172

  65. Hasegawa T, Hayashi T (2013) Collaborative filtering based spot recommendation seamlessly available in home and away areas. In: 12th international conference on computer and information science (ICIS), 2013 IEEE/ACIS. IEEE, pp 547–548

  66. Sun J, Xiong Y, Zhu Y, Liu J, Guan C, Xiong H (2015) Multi-source information fusion for personalized restaurant recommendation. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 983–986

  67. Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. In: Advances in neural information processing systems, pp 556–562

  68. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model, Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 426–434

  69. Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems, pp 1257–1264

  70. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp 135–142

  71. Li L, Zhou Y, Xiong H, Hu C, Wei X (2017) Collaborative filtering based on user attributes and user ratings for restaurant recommendation. In: 2017 IEEE 2nd advanced information technology, electronic and automation control conference (IAEAC). IEEE, pp 2592–2597

  72. Katarya R, Verma OP (2015) Restaurant recommender system based on psychographic and demographic factors in mobile environment. In: 2015 international conference on green computing and Internet of Things (ICGCIoT). IEEE, pp 907–912

  73. da Silva EQ, Camilo-Junior CG, Pascoal LML, Rosa TC (2016) An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering. Expert Syst Appl 53:204

    Google Scholar 

  74. Li YM, Chou CL, Lin LF (2014) A social recommender mechanism for location-based group commerce. Inf Sci 274:125

    Google Scholar 

  75. Boratto L, Carta S, Fenu G, Saia R (2017) Semantics-aware content-based recommender systems: design and architecture guidelines. Neurocomputing 254:79

    Google Scholar 

  76. Brusilovski P, Kobsa A, Nejdl W (2007) The adaptive web: methods and strategies of web personalization, vol 4321. Springer, Berlin

    Google Scholar 

  77. Koetphrom N, Charusangvittaya P, Sutivong D (2018) Comparing filtering techniques in restaurant recommendation system. In: 2018 2nd international conference on engineering innovation (ICEI). IEEE, pp 46–51

  78. Dua D, Karra Taniskidou E (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 4 Feb 2019

  79. Mustafa AA, Budi I (2018) Recommendation system based on item and user similarity on restaurants directory online. In: 2018 6th international conference on information and communication technology (ICoICT). IEEE, pp 70–74

  80. Chu CH, Wu SH (2013) A Chinese restaurant recommendation system based on mobile context-aware services. In: 2013 IEEE 14th international conference on mobile data management (MDM), vol 2. IEEE, pp 116–118

  81. Gupta A, Singh K (2013) Location based personalized restaurant recommendation system for mobile environments. In: 2013 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 507–511

  82. Quercia D, Lathia N, Calabrese F, Di Lorenzo G, Crowcroft J (2010) Recommending social events from mobile phone location data. In: 2010 IEEE 10th international conference on data mining (ICDM). IEEE, pp 971–976

  83. Park MH, Hong JH, Cho SB (2007) Location-based recommendation system using Bayesian user’s preference model in mobile devices. In: International conference on ubiquitous intelligence and computing. Springer, pp 1130–1139

  84. Debnath S, Ganguly N, Mitra P (2008) Feature weighting in content based recommendation system using social network analysis. In: Proceedings of the 17th international conference on World Wide Web. ACM, pp 1041–1042

  85. Zeng J, Li F, Liu H, Wen J, Hirokawa S (2016) A restaurant recommender system based on user preference and location in mobile environment. In: 2016 5th IIAI international congress on advanced applied informatics (IIAI-AAI). IEEE, pp 55–60

  86. Taneja A, Gupta P, Garg A, Bansal A, Grewal KP, Arora A (2016) Social graph based location recommendation using users’ behavior: by locating the best route and dining in best restaurant. In: 2016 fourth international conference on parallel, distributed and grid computing (PDGC). IEEE, pp 488–494

  87. Miao X, Gao Y, Chen G, Cui H, Guo C, Pan W (2016) S i 2 p: a restaurant recommendation system using preference queries over incomplete information. Proc VLDB Endow 9(13):1509

    Google Scholar 

  88. Zhang C, Zhang H, Wang J (2018) Personalized restaurant recommendation method combining group correlations and customer preferences. Inf Sci 454:128

    Google Scholar 

  89. Wibowo V, Handayani MA (2018) Genetic algorithm for generating travel itinerary recommendation with restaurant selection. In: 2018 IEEE international conference on industrial engineering and engineering management (IEEM). IEEE, pp 427–431

  90. Tsiligirides T (1984) Heuristic methods applied to orienteering. J Oper Res Soc 35(9):797

    Google Scholar 

  91. Vansteenwegen P, Souffriau W, Van Oudheusden D (2011) The orienteering problem: a survey. Eur J Oper Res 209(1):1

    MathSciNet  MATH  Google Scholar 

  92. Goldberg DE, Lingle R et al (1985) Alleles, loci, and the traveling salesman problem. In: Proceedings of an international conference on genetic algorithms and their applications, Lawrence Erlbaum, Hillsdale, NJ, vol 154, pp 154–159

  93. Weng L, Huang Z (2018) A study of tourism advertising effects: advertising formats and destination types

  94. Bowie D, Chang JC (2005) Tourist satisfaction: a view from a mixed international guided package tour. J Vacat Mark 11(4):303

    Google Scholar 

  95. 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

    Google Scholar 

  96. Lucas JP, Laurent A, Moreno MN, Teisseire M (2012) A fuzzy associative classification approach for recommender systems. Int J Uncertain Fuzziness Knowl Based Syst 20(04):579

    MathSciNet  Google Scholar 

  97. Coelho B, Martins C, Almeida A (2009) Adaptive tourism modeling and socialization system. In: 2009 international conference on computational science and engineering. IEEE, pp 645–652

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

    Google Scholar 

  99. 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

    Google Scholar 

  100. Le QT, Pishva D (2016) An innovative tour recommendation system for tourists in Japan. In: 2016 18th international conference on advanced communication technology (ICACT). IEEE, pp 717–729

  101. Kotiloglu S, Lappas T, Pelechrinis K, Repoussis P (2017) Personalized multi-period tour recommendations. Tour Manag 62:76

    Google Scholar 

  102. Tricoire F, Romauch M, Doerner KF, Hartl RF (2010) Heuristics for the multi-period orienteering problem with multiple time windows. Comput Oper Res 37(2):351

    MathSciNet  MATH  Google Scholar 

  103. Cheng Z, Caverlee J, Lee K, Sui DZ (2011) Exploring millions of footprints in location sharing services. ICWSM 2011:81

    Google Scholar 

  104. Lorenzi F, Loh S, Abel M (2011) PersonalTour: a recommender system for travel packages. In: 2011 IEEE/WIC/ACM international conference on Web intelligence and intelligent agent technology (WI-IAT), vol 2. IEEE, pp 333–336

  105. 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

    Google Scholar 

  106. Tan C, Liu Q, Chen E, Xiong H, Wu X (2014) Object-oriented travel package recommendation. ACM Trans Intell Syst Technol (TIST) 5(3):43

    Google Scholar 

  107. Yu Z, Xu H, Yang Z, Guo B (2016) Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Trans Hum Mach Syst 46(1):151

    Google Scholar 

  108. Masthoff J (2015) Group recommender systems: aggregation, satisfaction and group attributes, in recommender systems handbook. Springer, Berlin, pp 743–776

    Google Scholar 

  109. Forsyth DR (2018) Group dynamics. Cengage Learning, Boston

    Google Scholar 

  110. De Pessemier T, Dhondt J, Martens L (2017) Hybrid group recommendations for a travel service. Multimed Tools Appl 76(2):2787

    Google Scholar 

  111. Bangor A, Kortum PT, Miller JT (2008) An empirical evaluation of the system usability scale. Int Journal of Hum Comput Interaction 24(6):574

    Google Scholar 

  112. Delic A, Neidhardt J (2017) A comprehensive approach to group recommendations in the travel and tourism domain. In: Adjunct publication of the 25th conference on user modeling, adaptation and personalization. ACM, pp 11–16

  113. Delic A, Neidhardt J, Nguyen TN, Ricci F (2018) An observational user study for group recommender systems in the tourism domain. Inf Technol Tour 19(1–4):87

    Google Scholar 

  114. Delic A, Neidhardt J, Werthner H (2018) Group decision making and group recommendations. In: 2018 IEEE 20th conference on business informatics (CBI), vol 1. IEEE, pp 79–88

  115. Varfolomeyev A, Korzun D, Ivanovs A, Petrina O, Arapatsakos C, Razeghi M, Gekas V (2014) Smart personal assistant for historical tourism. In: Proceedings of 2nd international conference on environment, energy, ecosystems and development (EEEAD’2014), pp 9–15

  116. Varfolomeyev AG, Ivanovs A, Korzun DG, Petrina OB (2015) Smart spaces approach to development of recommendation services for historical e-tourism. UBICOMM 2015:69

    Google Scholar 

  117. Varfolomeyev A, Korzun D, Ivanovs A, Soms H, Petrina O (2015) Smart space based recommendation service for historical tourism. Procedia Comput Sci 77:85

    Google Scholar 

  118. Kulakov K, Petrina O (2015) Ontological model of multi-source smart space content for use in cultural heritage trip planning. In: 2015 17th conference of open innovations association (FRUCT). IEEE, pp 96–103

  119. Honkola J, Laine H, Brown R, Tyrkkö O (2010) Smart-M3 information sharing platform. In: 2010 IEEE symposium on computers and communications (ISCC). IEEE, pp 1041–1046

  120. Binucci C, De Luca F, Di Giacomo E, Liotta G, Montecchiani F (2017) Designing the content analyzer of a travel recommender system. Expert Syst Appl 87:199

    Google Scholar 

  121. Mathias M, Moussa A, Zhou F, Torres-Moreno JM, Poli MS, Josselin D, El-Bèze M, Linhares AC, Rigat F (2014) Optimisation using natural language processing: Personalized tour recommendation for museums. In: 2014 federated conference on computer science and information systems. IEEE, pp 439–446

  122. Benouaret I, Lenne D (2015) Personalizing the museum experience through context-aware recommendations. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 743–748

  123. Rossi S, Barile F, Galdi C, Russo L (2016) Artworks sequences recommendations for groups in museums. In: 2016 12th international conference on signal-image technology & internet-based systems (SITIS). IEEE, pp 455–462

  124. Kovavisaruch L.o, Sanpechuda T, Chinda K, Sornlertlamvanich V, Kamonvej P (2016) Museum content evaluation based on visitor behavior. In: 2016 13th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON). IEEE, pp 1–5

  125. Kosmopoulos D, Styliaras G (2018) A survey on developing personalized content services in museums. Pervasive Mob Comput 47:54

    Google Scholar 

  126. Tsiviltidou Z (2015) Digital storytelling with mobile media for inquiry-based museum learning: the student as author of the museum experience. In: 2015 international conference on interactive mobile communication technologies and learning (IMCL). IEEE, pp 91–95

  127. Naudet Y, Antoniou A, Lykourentzou I, Tobias E, Rompa J, Lepouras G (2015) Museum personalization based on gaming and cognitive styles: the BLUE experiment. Int J Virtual Communities Soc Netw (IJVCSN) 7(2):1

    Google Scholar 

  128. Minkov E, Kahanov K, Kuflik T (2017) Graph-based recommendation integrating rating history and domain knowledge: application to on-site guidance of museum visitors. J Assoc Inf Sci Technol 68(8):1911

    Google Scholar 

  129. Tsiviltidou Z, Vavoula G (2017) Digital storytelling as a framework for inquiry-based museum learning. In: 2017 IEEE 17th international conference on advanced learning technologies (ICALT). IEEE, pp 403–405

  130. Yang WS, Hwang SY (2013) iTravel: a recommender system in mobile peer-to-peer environment. J Syst Softw 86(1):12

    Google Scholar 

  131. Zhang Z, Pan H, Xu G, Wang Y, Zhang P (2016) A context-awareness personalized tourist attraction recommendation algorithm. Cybern Inf Technol 16(6):146

    Google Scholar 

  132. Wang X, Li XR, Zhen F, Zhang J (2016) How smart is your tourist attraction? Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach. Tour Manag 54:309

    Google Scholar 

  133. Chen YP, Loh CH, Chen WY, Ho WJ, Chen YC (2016) Skyline query-based attraction recommendation system: an example on design and development of the demodulation and encoding heritage system. In: 2016 international computer symposium (ICS). IEEE, pp 318–321

  134. Oh KJ, Kim Z, Oh H, Lim CG, Gweon G (2016) Travel intention-based attraction network for recommending travel destinations. In: 2016 international conference on big data and smart computing (BigComp). IEEE, pp 277–280

  135. Zhu Z, Cao J, Weng C (2018) Location-time-sociality aware personalized tourist attraction recommendation in LBSN. In: 2018 IEEE 22nd international conference on computer supported cooperative work in design (CSCWD). IEEE, pp 636–641

  136. Mikhailov S, Kashevnik A (2018) Smartphone-based tourist trip planning system: a context-based approach to offline attraction recommendation. In: MATEC web of conferences, vol 161. EDP Sciences, p 03026

  137. Herzog D, Wörndl W (2015) Spontaneous event recommendations on the go. In: DMRS, pp 1–4

  138. Ogundele TJ, Chow CY, Zhang JD (2017) EventRec: personalized event recommendations for smart event-based social networks. In: 2017 IEEE international conference on smart computing (SMARTCOMP). IEEE, pp 1–8

  139. Ogundele TJ, Chow CY, Zhang JD (2018) SoCaST*: personalized event recommendations for event-based social networks: a multi-criteria decision making approach. IEEE Access 6:27579

    Google Scholar 

  140. Kontukoski M, Paakki M, Thureson J, Uimonen H, Hopia A (2016) Imagined salad and steak restaurants: consumers’ colour, music and emotion associations with different dishes. Int J Gastron Food Sci 4:1

    Google Scholar 

  141. Sillani S, Miccoli A, Nassivera F (2017) Different preferences for wine communication. Wine Econ Policy 6(1):28

    Google Scholar 

  142. Rivolli A, Parker LC, de Carvalho AC (2017) Food truck recommendation using multi-label classification. In: Portuguese conference on artificial intelligence. Springer, pp 585–596

  143. Datta R, Joshi D, Li J, Wang JZ (2006) Studying aesthetics in photographic images using a computational approach. In: European conference on computer vision. Springer, pp 288–301

  144. Zhang Y, Sun X, Yao H, Qin L, Huang Q (2012) Aesthetic composition represetation for portrait photographing recommendation. In: 2012 19th IEEE international conference on image processing (ICIP). IEEE, pp 2753–2756

  145. Ferrari V, Marin-Jimenez M, Zisserman A (2009) Pose search: retrieving people using their pose. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 1–8

  146. Obrador P, Schmidt-Hackenberg L, Oliver N (2010) The role of image composition in image aesthetics. In: 2010 17th IEEE international conference on image processing (ICIP). IEEE, pp 3185–3188

  147. Pang W M (2015) Context-aware scene recommendation for travel photography. In: Adjunct proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2015 ACM international symposium on wearable computers. ACM, pp 17–20

  148. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145

    MATH  Google Scholar 

  149. Frosh P (2015) Selfies| the gestural image: the selfie, photography theory, and kinesthetic sociability. Int J Commun 9:22

    Google Scholar 

  150. Hsieh YT, Yeh MC (2017) Head pose recommendation for taking good selfies. In: Proceedings of the workshop on multimodal understanding of social, affective and subjective attributes. ACM, pp 55–60

  151. Yeh MC, Lin HW (2014) Virtual portraitist: aesthetic evaluation of selfies based on angle. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 221–224

  152. Huang GB, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In: Workshop on faces in ‘Real-Life’ images: detection, alignment, and recognition

  153. Xiong X, De la Torre F (2013) Supervised descent method and its applications to face alignment. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 532–539

  154. Tzimiropoulos G, Pantic M (2013) Optimization problems for fast AAM fitting in-the-wild. In: Proceedings of the IEEE international conference on computer vision, pp 593–600

  155. Pallett PM, Link S, Lee K (2010) New “golden” ratios for facial beauty. Vis Res 50(2):149

    Google Scholar 

  156. Sayood K (2017) Introduction to data compression. Morgan Kaufmann, Burlington

    MATH  Google Scholar 

  157. Flugel JC (1930) The psychology of clothes, vol 18. Hogarth Press, London

    Google Scholar 

  158. Shen E, Lieberman H, Lam F (2007) What am I gonna wear? Scenario-oriented recommendation. In: Proceedings of the 12th international conference on Intelligent user interfaces. ACM, pp 365–368

  159. Zhang X, Jia J, Gao K, Zhang Y, Zhang D, Li J, Tian Q (2017) Trip outfits advisor: location-oriented clothing recommendation. IEEE Trans Multimed 19(11):2533

    Google Scholar 

  160. Simo-Serra E, Fidler S, Moreno-Noguer F, Urtasun R (2015) Neuroaesthetics in fashion: modeling the perception of fashionability. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 869–877

  161. Jagadeesh V, Piramuthu R, Bhardwaj A, Di W, Sundaresan N (2014) Large scale visual recommendations from street fashion images. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1925–1934

  162. Yamaguchi K, Hadi Kiapour M, Berg TL (2013) Paper doll parsing: retrieving similar styles to parse clothing items. In: Proceedings of the IEEE international conference on computer vision, pp 3519–3526

  163. Yamaguchi K, Kiapour MH, Ortiz LE, Berg TL (2012) Parsing clothing in fashion photographs. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3570–3577

  164. Yang W, Luo P, Lin L (2014) Clothing co-parsing by joint image segmentation and labeling. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3182–3189

  165. Liu S, Feng J, Domokos C, Xu H, Huang J, Hu Z, Yan S (2014) Fashion parsing with weak color-category labels. IEEE Trans Multimed 16(1):253

    Google Scholar 

  166. Yuan NJ, Zheng Y, Zhang L, Xie X (2013) T-finder: a recommender system for finding passengers and vacant taxis. IEEE Trans Knowl Data Eng 25(10):2390

    Google Scholar 

  167. Lu Y, Wu H, Xin L, Chen P, Zhang J (2019) TourSense: a framework for tourist identification and analytics using transport data. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2019.2894131

    Article  Google Scholar 

  168. Rittichainuwat BN, Chakraborty G (2009) Perceived travel risks regarding terrorism and disease: the case of Thailand. Tour Manag 30(3):410

    Google Scholar 

  169. Mansfeld Y et al (2006) The role of security information in tourism crisis management: the missing link. In: Mansfeld Y, Pizam A (eds) Tourism, security & safety: from theory to practice. Taylor & Francis, London, pp 271–290

    Google Scholar 

  170. Beecroft M, Pangbourne K (2015) Personal security in travel by public transport: the role of traveller information and associated technologies. IET Intell Transp Syst 9(2):167

    Google Scholar 

  171. Liu H, Hu J, Rauterberg M (2015) Follow your heart: heart rate controlled music recommendation for low stress air travel. Interaction Stud 16(2):303

    Google Scholar 

  172. Kaplan BM, Langendorf R, Lev M, Pick A (1973) Tachycardia-bradycardia syndrome (so-called “sick sinus syndrome”): pathology, mechanisms and treatment. Am J Cardiol 31(4):497

    Google Scholar 

  173. Braunhofer M, Elahi M, Ge M, Ricci F, Schievenin T (2013) STS: design of weather-aware mobile recommender systems in tourism. In: AI* HCI@ AI* IA

  174. Braunhofer V, Elahi M, Ricci F, Schievenin T (2013) Context-aware points of interest suggestion with dynamic weather data management. In: Information and communication technologies in tourism 2014. Springer, pp 87–100

  175. Rosselló J, Sansó A (2017) Yearly, monthly and weekly seasonality of tourism demand: a decomposition analysis. Tour Manag 60:379

    Google Scholar 

  176. Yang D, Zhang D, Qu B (2016) Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Trans Intell Syst Technol (TIST) 7(3):30

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Technology, Nirma University, Ahmedabad, Gujarat, 382 481, India

    Kinjal Chaudhari & Ankit Thakkar

Authors
  1. Kinjal Chaudhari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ankit Thakkar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kinjal Chaudhari.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaudhari, K., Thakkar, A. A Comprehensive Survey on Travel Recommender Systems. Arch Computat Methods Eng 27, 1545–1571 (2020). https://doi.org/10.1007/s11831-019-09363-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-019-09363-7

Search

Navigation