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Cross-cultural contextualisation for recommender systems

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Abstract

Cultural Heritage (CH) domain is rapidly moving from traditional heritage sites into smart cultural heritage environment through various technologies. As one of the important technologies in the smart space, Recommender Systems (RSs) have been widely utilised to personalised services and matching visitors’ goals and behaviours. Whereas, cultural difference is often considered a barrier to technology transfer or adoption. However, few studies focus on how the cultural factor influences recommendation despite cultural difference largely affects user preferences in the RSs. Furthermore, existing researches have mainly analysed evaluation results of their recommendation to reveal cultural differences, rather than utilising the cross-cultural factors into RSs. In this paper, we propose a novel concept of cross-cultural contextualisation and a model to compute the cross-cultural factor affecting users (countries or cultures) preferences by using matrix factorisation and clustering techniques. In addition, we discuss how to apply the model to RSs in CH domain through cross-domain recommendation techniques. Note that the two computational techniques were used to analyse cross-cultural factors which impact to user preferences, rather than to recommend items. In other words, the proposed model and computing results capable of utilisation into the other RSs as well as various research fields. Results of experiments with a real-world dataset showed effectiveness of the proposed model and supported that there is cultural difference influencing users’ rating behaviours. Furthermore, a systematic analysis of dataset and the experimental results presented that individual users could be considered as country-wise groups for the model to analyse the cross-cultural factors.

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Notes

  1. https://www.kaggle.com/residentmario/exploring-tripadvisor-uk-restaurant-reviews

  2. http://www.alexa.com/siteinfo/tripadvisor.com, Retrieved: July 9th, 2019

  3. https://en.wikipedia.org/wiki/Continent

References

  • Amato F, Moscato V, Picariello A, Colace F, Santo MD, Schreiber FA, Tanca L (2017) Big data meets digital cultural heritage: design and implementation of scrabs, a smart context-aware browsing assistant for cultural environments. J Comput Cult Herit (JOCCH) 10(1):6

    Google Scholar 

  • Bartolini I, Moscato V, Pensa RG, Penta A, Picariello A, Sansone C, Sapino ML (2013) Recommending multimedia objects in cultural heritage applications. In: Petrosino A, Maddalena L, Pala P (eds) Proceedings of the 2013 international workshops on new trends in image analysis and processing ICIAP 2013, vol 8158. Lecture notes in computer science. Springer, Naples, Italy, pp 257–267. https://doi.org/10.1007/978-3-642-41190-8_28

  • Bartolini I, Moscato V, Pensa RG, Penta A, Picariello A, Sansone C, Sapino ML (2016) Recommending multimedia visiting paths in cultural heritage applications. Multimedia Tools Appl 75(7):3813–3842. https://doi.org/10.1007/s11042-014-2062-7

    Article  Google Scholar 

  • Bekele MK, Pierdicca R, Frontoni E, Malinverni ES, Gain J (2018) A survey of augmented, virtual, and mixed reality for cultural heritage. JOCCH 11(2):7:1–7:36. https://doi.org/10.1145/3145534

    Article  Google Scholar 

  • Berkovsky S, Taib R, Hijikata Y, Braslavski P, Knijnenburg BP (2018) A cross-cultural analysis of trust in recommender systems. In: Mitrovic T, Zhang J, Chen L, Chin D (eds) Proceedings of the 26th conference on user modeling, adaptation and personalization, UMAP 2018, ACM, Singapore, pp 285–289. https://doi.org/10.1145/3209219.3209251

  • Breese JS, Heckerman D, Kadie CM (2013) Empirical analysis of predictive algorithms for collaborative filtering. CoRR. arXiv:abs/1301.7363

  • Cardoso PJS, Guerreiro PMM, Monteiro JM, Rodrigues JMF (2018) Applying an implicit recommender system in the preparation of visits to cultural heritage places. In: Antona M, Stephanidis C (eds) Proceedings of the 12th international conference on universal access in human-computer interaction (UAHCI 2018), vol 10908. Lecture notes in computer science. Springer, Las Vegas, NV, USA, pp 421–436. https://doi.org/10.1007/978-3-319-92052-8_33

  • Chen L, Pu P (2008) A cross-cultural user evaluation of product recommender interfaces. In: Pu P, Bridge DG, Mobasher B, Ricci F (eds) Proceedings of the 2008 ACM conference on recommender systems, RecSys 2008, ACM, Lausanne, Switzerland, pp 75–82. https://doi.org/10.1145/1454008.1454022

  • Chen L, Pu P (2014) Experiments on user experiences with recommender interfaces. Behav IT 33(4):372–394. https://doi.org/10.1080/0144929X.2012.719034

    Article  MathSciNet  Google Scholar 

  • Chianese A, Piccialli F (2014) Smach: a framework for smart cultural heritage spaces. In: Yétongnon K, Dipanda A, Chbeir R (eds) Proceedings of the 10th international conference on signal-image technology and internet-based systems, SITIS 2014, IEEE Computer Society, Marrakech, Morocco, pp 477–484. https://doi.org/10.1109/SITIS.2014.16

  • Chianese A, Piccialli F (2016) A smart system to manage the context evolution in the cultural heritage domain. Comput Electr Eng 55:27–38. https://doi.org/10.1016/j.compeleceng.2016.02.008

    Article  Google Scholar 

  • Chianese A, Piccialli F, Valente I (2015) Smart environments and cultural heritage: a novel approach to create intelligent cultural spaces. J Locat Based Serv 9(3):209–234. https://doi.org/10.1080/17489725.2015.1099752

    Article  Google Scholar 

  • Chianese A, Piccialli F, Jung JE (2016) The internet of cultural things: towards a smart cultural heritage. In: Yétongnon K, Dipanda A, Chbeir R, Pietro GD, Gallo L (eds) Proceedings of the 12th international conference on signal-image technology & internet-based systems, SITIS 2016, IEEE Computer Society, Naples, Italy, pp 493–496. https://doi.org/10.1109/SITIS.2016.83

  • Choi J, Lee HJ, Sajjad F, Lee H (2014) The influence of national culture on the attitude towards mobile recommender systems. Technol Forecast Soc Change 86:65–79

    Article  Google Scholar 

  • Chu W, Huang W (2017) Cultural difference and visual information on hotel rating prediction. World Wide Web 20(4):595–619. https://doi.org/10.1007/s11280-016-0404-2

    Article  Google Scholar 

  • Heimbürger A, Kiyoki Y (2014) Context and user-centered approaches: icons in cross-cultural context. In: Brézillon P, Gonzalez AJ (eds) Context in computing—a cross-disciplinary approach for modeling the real world. Springer, Berlin, pp 309–325. https://doi.org/10.1007/978-1-4939-1887-4_20

    Chapter  Google Scholar 

  • Herlocker JL, Konstan JA, Borchers A, Riedl J (2017) An algorithmic framework for performing collaborative filtering. SIGIR Forum 51(2):227–234. https://doi.org/10.1145/3130348.3130372

    Article  Google Scholar 

  • Hofstede G (1980) Culture and organizations. Int Stud Manag Organ 10(4):15–41

    Google Scholar 

  • Hofstede G, Bond MH (1988) The confucius connection: from cultural roots to economic growth. Organ Dyn 16(4):5–21

    Article  Google Scholar 

  • Hong M, Jung JJ (2018) Multi-sided recommendation based on social tensor factorization. Inf Sci 447:140–156. https://doi.org/10.1016/j.ins.2018.03.019

    Article  Google Scholar 

  • Hong M, Jung JJ, Piccialli F, Chianese A (2017) Social recommendation service for cultural heritage. Pers Ubiquitous Comput 21(2):191–201. https://doi.org/10.1007/s00779-016-0985-x

    Article  Google Scholar 

  • Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C (2013) Personalized recommendation via cross-domain triadic factorization. In: Schwabe D, Almeida VAF, Glaser H, Baeza-Yates RA, Moon SB (eds) Proceedings of the 22nd international world wide web conference, WWW ’13, international world wide web conferences steering committee/ACM, Rio de Janeiro, Brazil, pp 595–606. https://doi.org/10.1145/2488388.2488441

  • Jung TH, Lee H, Chung N, tom Dieck MC (2018) Cross-cultural differences in adopting mobile augmented reality at cultural heritage tourism sites. Int J Contemp Hosp Manag 30(3):1621–1645

    Article  Google Scholar 

  • Lee LYS (2016) Hospitality industry web-based self-service technology adoption model: a cross-cultural perspective. J Hosp Tour Res 40(2):162–197

    Article  MathSciNet  Google Scholar 

  • Li B (2011) Cross-domain collaborative filtering: a brief survey. In: Proceedings of the IEEE 23rd international conference on tools with artificial intelligence, ICTAI 2011, IEEE Computer Society, Boca Raton, FL, USA, pp 1085–1086. https://doi.org/10.1109/ICTAI.2011.184

  • Li M (2014) Cross-cultural tourist research: a meta-analysis. J Hosp Tour Res 38(1):40–77

    Article  Google Scholar 

  • Nguyen TT, Camacho D, Jung JE (2017) Identifying and ranking cultural heritage resources on geotagged social media for smart cultural tourism services. Pers Ubiquitous Comput 21(2):267–279. https://doi.org/10.1007/s00779-016-0992-y

    Article  Google Scholar 

  • Park J, Lee O, Jung JE (2018) Spatio-temporal query contextualization for microtext retrieval in social media. Concurr Comput Pract Exp 30(15):e4458. https://doi.org/10.1002/cpe.4458

    Article  Google Scholar 

  • Pavlidis G (2019) Recommender systems, cultural heritage applications, and the way forward. J Cult Herit 35:183–196

    Article  Google Scholar 

  • Pu P, Chen L (2006) Trust building with explanation interfaces. In: Paris C, Sidner CL (eds) Proceedings of the 11th international conference on intelligent user interfaces, IUI 2006, ACM, Sydney, Australia, pp 93–100. https://doi.org/10.1145/1111449.1111475

  • Rajaonarivo L, Fonteles A, Sallaberry C, Bessagnet MN, Roose P, Etcheverry P, Marquesuzaà C, Lacayrelle ALP, Cayèré C, Coudert Q (2019) Recommendation of heterogeneous cultural heritage objects for the promotion of tourism. ISPRS Int J Geo Inf 8(5):230

    Article  Google Scholar 

  • Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, Berlin, pp 1–34. https://doi.org/10.1007/978-1-4899-7637-6_1

    Chapter  Google Scholar 

  • Ruotsalo T, Haav K, Stoyanov A, Roche S, Fani E, Deliai R, Mäkelä E, Kauppinen T, Hyvönen E (2013) SMARTMUSEUM: a mobile recommender system for the web of data. J Web Semant 20:50–67. https://doi.org/10.1016/j.websem.2013.03.001

    Article  Google Scholar 

  • Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: Järvelin K, Beaulieu M, Baeza-Yates RA, Myaeng S (eds) Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR 2002, ACM, Tampere, Finland, pp 253–260. https://doi.org/10.1145/564376.564421

  • Siountri K, Skondras E, Vergados DD (2018) A delivery model for cultural heritage services in smart cities environments. In: Ioannides M, Fink EE, Brumana R, Patias P, Doulamis A, Martins J, Wallace M (eds) Proceedings of the 7th international conference on digital heritage. Progress in cultural heritage: documentation, preservation, and protection, EuroMed 2018, vol 11197. Lecture Notes in Computer Science, Springer, Nicosia, Cyprus, pp 279–288. https://doi.org/10.1007/978-3-030-01765-1_31

  • Smirnov AV, Kashevnik AM, Ponomarev A (2017) Context-based infomobility system for cultural heritage recommendation: tourist assistant—TAIS. Pers Ubiquitous Comput 21(2):297–311. https://doi.org/10.1007/s00779-016-0990-0

    Article  Google Scholar 

  • Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:421425:1–421425:19. https://doi.org/10.1155/2009/421425

    Article  Google Scholar 

  • Su X, Sperli G, Moscato V, Picariello A, Esposito C, Choi C (2019) An edge intelligence empowered recommender system enabling cultural heritage applications. IEEE Trans Ind Inf 15(7):4266–4275

    Article  Google Scholar 

  • Tang TY, Winoto P, Ye RZ (2011) Analysis of a multi-domain recommender system. In: Proceedings of the 3rd international conference on data mining and intelligent information technology applications, IEEE, Macao, China, pp 280–285

  • Wang Y, Dai X, Jung JJ, Choi C (2018) Performance analysis of smart cultural heritage protection oriented wireless networks. Future Gen Comp Syst 81:593–600. https://doi.org/10.1016/j.future.2017.04.007

    Article  Google Scholar 

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Acknowledgements

This research was supported by the Chung-Ang University Research Grants in 2018. Also, This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A2B4010774).

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Authors and Affiliations

  1. Big Data Research Group, Western Norway Research Institute, Vestlandsforsking, Box 163, 6851, Sogndal, Norway

    Minsung Hong & Rajendra Akerkar

  2. Department of Computer Science and Engineering, Chung-Ang University, 84 Heukseok-ro, Heukseok-dong, Dongjak-gu, Seoul, South Korea

    Sojung An & Jason J. Jung

  3. Information Systems Department, Technical University of Madrid, 28031, Madrid, Spain

    David Camacho

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  1. Minsung Hong
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  2. Sojung An
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  5. Jason J. Jung
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Correspondence to Jason J. Jung.

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Hong, M., An, S., Akerkar, R. et al. Cross-cultural contextualisation for recommender systems. J Ambient Intell Human Comput 15, 1659–1670 (2024). https://doi.org/10.1007/s12652-019-01479-9

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