BITOUR: A Business Intelligence Platform for Tourism Analysis

[EN] Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination.This work has been supported by COLCIENCIAS through a PhD scholarship. This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Bustamante, A.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2020). BITOUR: A Business Intelligence Platform for Tourism Analysis. ISPRS International Journal of Geo-Information. 9(11):1-23. https://doi.org/10.3390/ijgi9110671S123911Nakahira, K. T., Akahane, M., & Fukami, Y. (2012). The Difference and Limitation of Cognition for Piano Playing Skill with Difference Educational Design. Smart Innovation, Systems and Technologies, 609-617. doi:10.1007/978-3-642-29934-6_59Chua, A., Servillo, L., Marcheggiani, E., & Moere, A. V. (2016). Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy. Tourism Management, 57, 295-310. doi:10.1016/j.tourman.2016.06.013Karagiannakis, N., Giannopoulos, G., Skoutas, D., & Athanasiou, S. (2015). OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap. Proceedings of the 9th ACM Conference on Recommender Systems. doi:10.1145/2792838.2796555Burcher, M., & Whelan, C. (2017). Social network analysis as a tool for criminal intelligence: understanding its potential from the perspectives of intelligence analysts. Trends in Organized Crime, 21(3), 278-294. doi:10.1007/s12117-017-9313-8Alcabnani, S., Oubezza, M., & Elkafi, J. (2019). An approach for the implementation of semantic Big Data Analytics in the Social Business Intelligence process on distributed environments (Cloud computing). Proceedings of the 4th International Conference on Big Data and Internet of Things. doi:10.1145/3372938.3373003Zeng, B., & Gerritsen, R. (2014). What do we know about social media in tourism? A review. Tourism Management Perspectives, 10, 27-36. doi:10.1016/j.tmp.2014.01.001Lalicic, L. (2018). Open innovation platforms in tourism: how do stakeholders engage and reach consensus? International Journal of Contemporary Hospitality Management, 30(6), 2517-2536. doi:10.1108/ijchm-04-2016-0233Dwyer, L., & Kim, C. (2003). Destination Competitiveness: Determinants and Indicators. Current Issues in Tourism, 6(5), 369-414. doi:10.1080/13683500308667962Gomezelj, D. O., & Mihalič, T. (2008). Destination competitiveness—Applying different models, the case of Slovenia. Tourism Management, 29(2), 294-307. doi:10.1016/j.tourman.2007.03.009Zhong, L., Deng, J., & Xiang, B. (2008). Tourism development and the tourism area life-cycle model: A case study of Zhangjiajie National Forest Park, China. Tourism Management, 29(5), 841-856. doi:10.1016/j.tourman.2007.10.002Fernández, J. I. P., & Rivero, M. S. (2009). Measuring Tourism Sustainability: Proposal for a Composite Index. Tourism Economics, 15(2), 277-296. doi:10.5367/000000009788254377Cibinskiene, A., & Snieskiene, G. (2015). Evaluation of City Tourism Competitiveness. Procedia - Social and Behavioral Sciences, 213, 105-110. doi:10.1016/j.sbspro.2015.11.411Business Intelligence (BI)—Glossaryhttps://www.gartner.com/it-glossary/business-intelligence-bi/Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 30(12), 3514-3554. doi:10.1108/ijchm-07-2017-0461Maeda, T. N., Yoshida, M., Toriumi, F., & Ohashi, H. (2016). Decision Tree Analysis of Tourists’ Preferences Regarding Tourist Attractions Using Geotag Data from Social Media. Proceedings of the Second International Conference on IoT in Urban Space. doi:10.1145/2962735.2962745Guy, I., Mejer, A., Nus, A., & Raiber, F. (2017). Extracting and Ranking Travel Tips from User-Generated Reviews. Proceedings of the 26th International Conference on World Wide Web. doi:10.1145/3038912.3052632Peng, M. Y.-P., Tuan, S.-H., & Liu, F.-C. (2017). Establishment of Business Intelligence and Big Data Analysis for Higher Education. Proceedings of the International Conference on Business and Information Management - ICBIM 2017. doi:10.1145/3134271.3134296Castellanos, M., Gupta, C., Wang, S., Dayal, U., & Durazo, M. (2012). A platform for situational awareness in operational BI. Decision Support Systems, 52(4), 869-883. doi:10.1016/j.dss.2011.11.011Cohen, L. (2017). Impacts of business intelligence on population health. Proceedings of the South African Institute of Computer Scientists and Information Technologists on - SAICSIT ’17. doi:10.1145/3129416.3129441Love, M., Boisvert, C., Uruchurtu, E., & Ibbotson, I. (2016). Nifty with Data. Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education. doi:10.1145/2899415.2899431Berndt, D. J., Hevner, A. R., & Studnicki, J. (s. f.). Hospital discharge transactions: a data warehouse component. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. doi:10.1109/hicss.2000.926791Musa, G. J., Chiang, P.-H., Sylk, T., Bavley, R., Keating, W., Lakew, B., … Hoven, C. W. (2013). Use of GIS Mapping as a Public Health Tool–-From Cholera to Cancer. Health Services Insights, 6, HSI.S10471. doi:10.4137/hsi.s10471Mooney, S. J., Westreich, D. J., & El-Sayed, A. M. (2015). Commentary. Epidemiology, 26(3), 390-394. doi:10.1097/ede.0000000000000274Wisniewski, M. F., Kieszkowski, P., Zagorski, B. M., Trick, W. E., Sommers, M., & Weinstein, R. A. (2003). Development of a Clinical Data Warehouse for Hospital Infection Control. Journal of the American Medical Informatics Association, 10(5), 454-462. doi:10.1197/jamia.m1299Miah, S. J., Vu, H. Q., Gammack, J., & McGrath, M. (2017). A Big Data Analytics Method for Tourist Behaviour Analysis. Information & Management, 54(6), 771-785. doi:10.1016/j.im.2016.11.011Li, D., Deng, L., & Cai, Z. (2019). Statistical analysis of tourist flow in tourist spots based on big data platform and DA-HKRVM algorithms. Personal and Ubiquitous Computing, 24(1), 87-101. doi:10.1007/s00779-019-01341-xKrawczyk, M., & Xiang, Z. (2015). Perceptual mapping of hotel brands using online reviews: a text analytics approach. Information Technology & Tourism, 16(1), 23-43. doi:10.1007/s40558-015-0033-0Alaei, A. R., Becken, S., & Stantic, B. (2017). Sentiment Analysis in Tourism: Capitalizing on Big Data. Journal of Travel Research, 58(2), 175-191. doi:10.1177/0047287517747753Thelwall, M. (2019). Sentiment Analysis for Tourism. Big Data and Innovation in Tourism, Travel, and Hospitality, 87-104. doi:10.1007/978-981-13-6339-9_6Höpken, W., Fuchs, M., Höll, G., Keil, D., & Lexhagen, M. (2013). Multi-Dimensional Data Modelling for a Tourism Destination Data Warehouse. Information and Communication Technologies in Tourism 2013, 157-169. doi:10.1007/978-3-642-36309-2_14Sabou, M., Onder, I., Brasoveanu, A. M. P., & Scharl, A. (2015). Towards Cross-Domain Decision Making in Tourism: A Linked Data Based Approach. SSRN Electronic Journal. doi:10.2139/ssrn.2580242Fermoso, A. M., Mateos, M., Beato, M. E., & Berjón, R. (2015). Open linked data and mobile devices as e-tourism tools. A practical approach to collaborative e-learning. Computers in Human Behavior, 51, 618-626. doi:10.1016/j.chb.2015.02.032Wöber, K. W. (2003). Information supply in tourism management by marketing decision support systems. Tourism Management, 24(3), 241-255. doi:10.1016/s0261-5177(02)00071-7Vajirakachorn, T., & Chongwatpol, J. (2017). Application of business intelligence in the tourism industry: A case study of a local food festival in Thailand. Tourism Management Perspectives, 23, 75-86. doi:10.1016/j.tmp.2017.05.003Diakopoulos, N., Naaman, M., & Kivran-Swaine, F. (2010). Diamonds in the rough: Social media visual analytics for journalistic inquiry. 2010 IEEE Symposium on Visual Analytics Science and Technology. doi:10.1109/vast.2010.5652922Bustamante, A., Sebastia, L., & Onaindia, E. (2019). Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks. Sensors, 19(11), 2612. doi:10.3390/s19112612Yasseri, T., Quattrone, G., & Mashhadi, A. (2013). Temporal analysis of activity patterns of editors in collaborative mapping project of OpenStreetMap. Proceedings of the 9th International Symposium on Open Collaboration. doi:10.1145/2491055.2491068Jilani, M., Corcoran, P., & Bertolotto, M. (2013). Multi-granular Street Network Representation towards Quality Assessment of OpenStreetMap Data. Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science - IWCTS ’13. doi:10.1145/2533828.2533833Luxen, D., & Vetter, C. (2011). Real-time routing with OpenStreetMap data. Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’11. doi:10.1145/2093973.2094062Baumbach, S., Rubel, C., Ahmed, S., & Dengel, A. (2019). Geospatial Customer, Competitor and Supplier Analysis for Site Selection of Supermarkets. Proceedings of the 2019 2nd International Conference on Geoinformatics and Data Analysis. doi:10.1145/3318236.3318264Milot, J., Munroe, P., Beaudry, E., Grondin, F., & Bourdeau, G. (2016). Lookupia. Proceedings of the 25th International Conference Companion on World Wide Web - WWW ’16 Companion. doi:10.1145/2872518.2890485Ciepluch, B., Mooney, P., Jacob, R., & Winstanley, A. C. (2009). Using OpenStreetMap to deliver location-based environmental information in Ireland. SIGSPATIAL Special, 1(3), 17-22. doi:10.1145/1645424.1645428Del Pilar Salas-Zárate, M., López-López, E., Valencia-García, R., Aussenac-Gilles, N., Almela, Á., & Alor-Hernández, G. (2014). A study on LIWC categories for opinion mining in Spanish reviews. Journal of Information Science, 40(6), 749-760. doi:10.1177/0165551514547842Gambino, O. J., & Calvo, H. (2016). A Comparison Between Two Spanish Sentiment Lexicons in the Twitter Sentiment Analysis Task. Advances in Artificial Intelligence - IBERAMIA 2016, 127-138. doi:10.1007/978-3-319-47955-2_11Mooney, P., Corcoran, P., & Winstanley, A. C. (2010). Towards quality metrics for OpenStreetMap. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’10. doi:10.1145/1869790.1869875El-Ashmawy, K. l. A. (2016). TESTING THE POSITIONAL ACCURACY OF OPENSTREETMAP DATA FOR MAPPING APPLICATIONS. Geodesy and cartography, 42(1), 25-30. doi:10.3846/20296991.2015.116049

Store buildings as tourist attractions: mining retail meaning of store building pictures through a machine learning approach

The aim of this paper is to understand the extent to which a store building can function as a tourism attraction, using a large luxury department store as case research. The study draws upon the idea that people complete a hermeneutic circle to create an extraordinary tourism experience to share with others. The data gathering is based on the collection of pictures posted online on Flickr and and analysed using a machine learning approach. A sample of 1,557 pictures related to a specific area in London (UK) were collected and analysed by means of a cluster analysis in order to determine which objects are most photographed. Findings reveal that the store building of a luxury department store is the central object in the majority of pictures within a 1km radius of the store main entrance, which demonstrates the role of store building attractiveness in tourism experience. The theoretical contribution is that this is the first paper adding the exterior of the building as attribute of the department store, and demonstrating the role of department stores in place attractiveness

A big-data analytics method for capturing visitor activities and flows: the case of an island country

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Understanding how people move from one location to another is important both for smart city planners and destination managers. Big-data generated on social media sites have created opportunities for developing evidence-based insights that can be useful for decision-makers. While previous studies have introduced observational data analysis methods for social media data, there remains a need for method development—specifically for capturing people’s movement flows and behavioural details. This paper reports a study outlining a new analytical method, to explore people’s activities, behavioural, and movement details for people monitoring and planning purposes. Our method utilises online geotagged content uploaded by users from various locations. The effectiveness of the proposed method, which combines content capturing, processing and predicting algorithms, is demonstrated through a case study of the Fiji Islands. The results show good performance compared to other relevant methods and show applicability to national decisions and policies

Development of Context-Aware Recommenders of Sequences of Touristic Activities

En els últims anys, els sistemes de recomanació s'han fet omnipresents a la xarxa. Molts serveis web, inclosa la transmissió de pel·lícules, la cerca web i el comerç electrònic, utilitzen sistemes de recomanació per facilitar la presa de decisions. El turisme és una indústria molt representada a la xarxa. Hi ha diversos serveis web (e.g. TripAdvisor, Yelp) que es beneficien de la integració de sistemes recomanadors per ajudar els turistes a explorar destinacions turístiques. Això ha augmentat la investigació centrada en la millora dels recomanadors turístics per resoldre els principals problemes als quals s'enfronten. Aquesta tesi proposa nous algorismes per a sistemes recomanadors turístics que aprenen les preferències dels turistes a partir dels seus missatges a les xarxes socials per suggerir una seqüència d'activitats turístiques que s'ajustin a diversos contextes i incloguin activitats afins. Per aconseguir-ho, proposem mètodes per identificar els turistes a partir de les seves publicacions a Twitter, identificant les activitats experimentades en aquestes publicacions i perfilant turistes similars en funció dels seus interessos, informació contextual i períodes d'activitat. Aleshores, els perfils d'usuari es combinen amb un algorisme de mineria de regles d'associació per capturar relacions implícites entre els punts d'interès de cada perfil. Finalment, es fa un rànquing de regles i un procés de selecció d'un conjunt d'activitats recomanables. Es va avaluar la precisió de les recomanacions i l'efecte del perfil d'usuari. A més, ordenem el conjunt d'activitats mitjançant un algorisme multi-objectiu per enriquir l'experiència turística. També realitzem una segona fase d'anàlisi dels fluxos turístics a les destinacions que és beneficiós per a les organitzacions de gestió de destinacions, que volen entendre la mobilitat turística. En general, els mètodes i algorismes proposats en aquesta tesi es mostren útils en diversos aspectes dels sistemes de recomanació turística.En los últimos años, los sistemas de recomendación se han vuelto omnipresentes en la web. Muchos servicios web, incluida la transmisión de películas, la búsqueda en la web y el comercio electrónico, utilizan sistemas de recomendación para ayudar a la toma de decisiones. El turismo es una industria altament representada en la web. Hay varios servicios web (e.g. TripAdvisor, Yelp) que se benefician de la inclusión de sistemas recomendadores para ayudar a los turistas a explorar destinos turísticos. Esto ha aumentado la investigación centrada en mejorar los recomendadores turísticos y resolver los principales problemas a los que se enfrentan. Esta tesis propone nuevos algoritmos para sistemas recomendadores turísticos que aprenden las preferencias de los turistas a partir de sus mensajes en redes sociales para sugerir una secuencia de actividades turísticas que se alinean con diversos contextos e incluyen actividades afines. Para lograr esto, proponemos métodos para identificar a los turistas a partir de sus publicaciones en Twitter, identificar las actividades experimentadas en estas publicaciones y perfilar turistas similares en función de sus intereses, contexto información y periodos de actividad. Luego, los perfiles de usuario se combinan con un algoritmo de minería de reglas de asociación para capturar relaciones entre los puntos de interés que aparecen en cada perfil. Finalmente, un proceso de clasificación de reglas y selección de actividades produce un conjunto de actividades recomendables. Se evaluó la precisión de las recomendaciones y el efecto de la elaboración de perfiles de usuario. Ordenamos además el conjunto de actividades utilizando un algoritmo multi-objetivo para enriquecer la experiencia turística. También llevamos a cabo un análisis de los flujos turísticos en los destinos, lo que es beneficioso para las organizaciones de gestión de destinos, que buscan entender la movilidad turística. En general, los métodos y algoritmos propuestos en esta tesis se muestran útiles en varios aspectos de los sistemas de recomendación turística.In recent years, recommender systems have become ubiquitous on the web. Many web services, including movie streaming, web search and e-commerce, use recommender systems to aid human decision-making. Tourism is one industry that is highly represented on the web. There are several web services (e.g. TripAdvisor, Yelp) that benefit from integrating recommender systems to aid tourists in exploring tourism destinations. This has increased research focused on improving tourism recommender systems and solving the main issues they face. This thesis proposes new algorithms for tourism recommender systems that learn tourist preferences from their social media data to suggest a sequence of touristic activities that align with various contexts and include affine activities. To accomplish this, we propose methods for identifying tourists from their frequent Twitter posts, identifying the activities experienced in these posts, and profiling similar tourists based on their interests, contextual information, and activity periods. User profiles are then combined with an association rule mining algorithm for capturing implicit relationships between points of interest apparent in each profile. Finally, a rule ranking and activity selection process produces a set of recommendable activities. The recommendations were evaluated for accuracy and the effect of user profiling. We further order the set of activities using a multi-objective algorithm to enrich the tourist experience. We also carry out a second-stage analysis of tourist flows at destinations which is beneficial to destination management organisations seeking to understand tourist mobility. Overall, the methods and algorithms proposed in this thesis are shown to be useful in various aspects of tourism recommender systems

Store buildings as tourist attractions: mining retail meaning of store building pictures through a machine learning approach

The aim of this paper is to understand the extent to which a store building can function as a tourism attraction, using a large luxury department store as case research. The study draws upon the idea that people complete a hermeneutic circle to create an extraordinary tourism experience to share with others. The data gathering is based on the collection of pictures posted online on Flickr and and analysed using a machine learning approach. A sample of 1,557 pictures related to a specific area in London (UK) were collected and analysed by means of a cluster analysis in order to determine which objects are most photographed. Findings reveal that the store building of a luxury department store is the central object in the majority of pictures within a 1km radius of the store main entrance, which demonstrates the role of store building attractiveness in tourism experience. The theoretical contribution is that this is the first paper adding the exterior of the building as attribute of the department store, and demonstrating the role of department stores in place attractiveness

Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks

Tesis por compendioEn el día a día, las personas suelen confiar en recomendaciones, tradicionalmente aportadas por otras personas (familia, amigos, etc.) para sus decisiones más variadas. En el mundo digital esto no es diferente, dado que los sistemas de recomendación están presentes en todas partes y de modo transparente. El principal objetivo de estos sistemas es el de ayudar en el proceso de toma de decisiones, generando recomendaciones de su interés y basadas en sus gustos. Dichas recomendaciones van desde productos en sitios web de comercio electrónico, como libros o lugares a visitar, además de qué comer o cuánto tiempo uno debe caminar al día para tener una vida sana, con quién salir o a quién seguir en las redes sociales. Esta es un área en ascensión. Por un lado, tenemos cada vez más usuarios en internet cuya vida está digitalizada, dado que lo que se hace en el "mundo real" está representado en cierto modo en el "mundo digital". Por otro lado, sufrimos una sobrecarga de información, que puede mitigarse mediante el uso de un sistema de recomendación. Sin embargo, estos sistemas también enfrentan algunos problemas, como el problema del arranque en frío y su necesidad de ser cada vez más "humanos", "personalizados" y "precisos" para satisfacer las exigencias de usuarios y empresas. En este desafiante escenario, los sistemas de recomendación basados en la personalidad se están estudiando cada vez más, ya que son capaces de enfrentar esos problemas. Algunos proyectos recientes proponen el uso de la personalidad humana en los recomendadores, ya sea en su conjunto o individualmente por rasgos. Esta tesis está dedicada a este nuevo área de recomendación basada en la personalidad, centrándose en uno de sus rasgos más importantes, la curiosidad. Además, para explotar la información ya existente en internet, obtendremos de forma implícita información de las redes sociales. Por lo tanto, este trabajo tiene como objetivo proporcionar una mejor experiencia al usuario final a través de un nuevo enfoque que ofrece una alternativa a algunos de los retos identificados en los sistemas de recomendación basados en la personalidad. Entre estas mejoras, el uso de las redes sociales para alimentar los sistemas de recomendación reduce el problema del arranque en frío y, al mismo tiempo, proporciona datos valiosos para la predicción de la personalidad humana. Por otro lado, la curiosidad no ha sido utilizada por ninguno de los sistemas de recomendación estudiados; casi todos han usado la personalidad general de un individuo a través de los Cinco Grandes rasgos de la personalidad. Sin embargo, los estudios psicológicos confirman que la curiosidad es un rasgo relevante en el proceso de elegir un item, cuestión directamente relacionada con los sistemas de recomendación. En resumen, creemos que un sistema de recomendación que mida implícitamente la curiosidad y la utilice en el proceso de recomendar nuevos ítems, especialmente en el sector turístico, podría claramente mejorar la capacidad de estos sistemas en términos de precisión, serendipidad y novedad, permitiendo a los usuarios obtener niveles positivos de satisfacción con las recomendaciones. Esta tesis realiza un estudio exhaustivo del estado del arte, donde destacamos trabajos sobre sistemas de recomendación, la personalidad humana desde el punto de vista de la psicología tradicional y positiva y finalmente cómo se combinan ambos aspectos. Luego, desarrollamos una aplicación en línea capaz de extraer implícitamente información del perfil de usuario en una red social, generando predicciones de uno o más rasgos de su personalidad. Finalmente, desarrollamos el sistema CURUMIM, capaz de generar recomendaciones en línea con diferentes propiedades, combinando la curiosidad y algunas características sociodemográficas (como el nivel de educación) extraídas de Facebook. El sistema ha sido probado y evaluado en el contexto turístico por usuarios rEn el dia a dia, les persones solen confiar en recomanacions, tradicionalment aportades per altres persones (família, amics, etc.) per a les seues decisions més variades. En el món digital això no és diferent, atès que els sistemes de recomanació estan presents a tot arreu i de manera transparent. El principal objectiu d'aquests sistemes és el d'ajudar en el procés de presa de decisions, generant recomanacions del seu interès i basades en els seus gustos. Aquestes recomanacions van des de productes en pàgines web de comerç electrònic, com a llibres o llocs a visitar, a més de què menjar o quant temps una persona ha de caminar al dia per a tindre una vida sana, amb qui eixir o a qui seguir en les xarxes socials. Aquesta és una àrea en ascensió. D'una banda, tenim cada vegada més usuaris en internet la vida de les quals està digitalitzada, atès que el que es fa en el "món real" està representat en certa manera en el "món digital". D'altra banda, patim una sobrecàrrega d'informació, que pot mitigar-se mitjançant l'ús d'un sistema de recomanació. No obstant això, aquests sistemes també enfronten alguns problemes, com el problema de l'arrencada en fred i la seua necessitat de ser cada vegada més "humans", "personalitzats" i "precisos" per a satisfer les exigències d'usuaris i empreses. En aquest desafiador escenari, els sistemes de recomanació basats en la personalitat s'estan estudiant cada vegada més, ja que són capaços d'enfrontar eixos problemes. Alguns projectes recents proposen l'ús de la personalitat humana en els recomendadors, ja siga en el seu conjunt o individualment per trets. Aquesta tesi està dedicada a aquest nou àrea de recomanació basada en la personalitat, centrant-se en un dels seus trets més importants, la curiositat. A més, per a explotar la informació ja existent en internet, obtindrem de forma implícita informació de les xarxes socials. Per tant, aquest treball té com a objectiu proporcionar una millor experiència a l'usuari final a través d'un nou enfocament que ofereix una alternativa a alguns dels reptes identificats en els sistemes de recomanació basats en la personalitat. Entre aquestes millores, l'ús de les xarxes socials per a alimentar els sistemes de recomanació redueix el problema de l'arrencada en fred i, al mateix temps, proporciona dades valuoses per a la predicció de la personalitat humana. D'altra banda, la curiositat no ha sigut utilitzada per cap dels sistemes de recomanació estudiats; quasi tots han usat la personalitat general d'un individu a través dels Cinc Grans trets de la personalitat. No obstant això, els estudis psicològics confirmen que la curiositat és un tret rellevant en el procés de triar un item, qüestió directament relacionada amb els sistemes de recomanació. En resum, creiem que un sistema de recomanació que mesure implícitament la curiositat i la utilitze en el procés de recomanar nous ítems, especialment en el sector turístic, podria clarament millorar la capacitat d'aquests sistemes en termes de precisió, sorpresa i novetat, permetent als usuaris obtindre nivells positius de satisfacció amb les recomanacions. Aquesta tesi realitza un estudi exhaustiu de l'estat de l'art, on destaquem treballs sobre sistemes de recomanació, la personalitat humana des del punt de vista de la psicologia tradicional i positiva i finalment com es combinen tots dos aspectes. Després, desenvolupem una aplicació en línia capaç d'extraure implícitament informació del perfil d'usuari en una xarxa social, generant prediccions d'un o més trets de la seua personalitat. Finalment, desenvolupem el sistema CURUMIM, capaç de generar recomanacions en línia amb diferents propietats, combinant la curiositat i algunes característiques sociodemogràfiques (com el nivell d'educació) extretes de Facebook. El sistema ha sigut provat i avaluat en el context turístic per usuaris reals. Els resultats demostren la seua capacitat perIn daily life, people usually rely on recommendations, traditionally given by other people (family, friends, etc.) for their most varied decisions. In the digital world, this is not different, given that recommender systems are present everywhere in such a way that we no longer realize. The main goal of these systems is to assist users in the decision-making process, generating recommendations that are of their interest and based on their tastes. These recommendations range from products in e-commerce websites, like books to read or places to visit to what to eat or how long one should walk a day to have a healthy life, who to date or who one should follow on social networks. And this is an increasing area. On the one hand, we have more and more users on the internet whose life is somewhat digitized, given than what one does in the "real world" is represented in a certain way in the "digital world". On the other hand, we suffer from information overload, which can be mitigated by the use of recommendation systems. However, these systems also face some problems, such as the cold start problem and their need to be more and more "human", "personalised" and "precise" in order to meet the yearning of users and companies. In this challenging scenario, personality-based recommender systems are being increasingly studied, since they are able to face these problems. Some recent projects have proposed the use of the human personality in recommenders, whether as a whole or individually by facet in order to meet those demands. Therefore, this thesis is devoted to this new area of personality-based recommendation, focusing on one of its most important traits, the curiosity. Additionally, in order to exploit the information already present on the internet, we will implicitly obtain information from social networks. Thus, this work aims to build a better experience for the end user through a new approach that offers an option for some of the gaps identified in personality-based recommendation systems. Among these gap improvements, the use of social networks to feed the recommender systems soften the cold start problem and, at the same time, it provides valuable data for the prediction of the human personality. Another found gap is that the curiosity was not used by any of the studied recommender systems; almost all of them have used the overall personality of an individual through the Big Five personality traits. However, psychological studies confirm that the curiosity is a relevant trait in the process of choosing an item, which is directly related to recommendation systems. In summary, we believe that a recommendation system that implicitly measures the curiosity and uses it in the process of recommending new items, especially in the tourism sector, could clearly improve the capacity of these systems in terms of accuracy, serendipity and novelty, allowing users to obtain positive levels of satisfaction with the recommendations. This thesis begins with an exhaustive study of the state of the art, where we highlight works about recommender systems, the human personality from the point of view of traditional and positive psychology and how these aspects are combined. Then, we develop an online application capable of implicitly extracting information from the user profile in a social network, thus generating predictions of one or more personality traits. Finally, we develop the CURUMIM system, able to generate online recommendations with different properties, combining the curiosity and some sociodemographic characteristics (such as level of education) extracted from Facebook. The system is tested and assessed within the tourism context by real users. The results demonstrate its ability to generate novel and serendipitous recommendations, while maintaining a good level of accuracy, independently of the degree of curiosity of the users.Menk Dos Santos, A. (2018). Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/114798TESISCompendi

Exploring urban visitors' mobilities. A multi-method approach

Aquesta tesi doctoral sorgeix de la necessitat d’aprofundir en el coneixement de les mobilitats dels visitants, entendre les decisions que configuren el seu comportament espacio-temporal i identificar i explorar els efectes que les seves mobilitats tenen sobre les destinacions urbanes. La tesi es desenvolupa entorn a quatre objectius específics que s’emmarquen en l’àmbit de recerca relacionat amb el seguiment de l’activitat dels visitants en destinacions turístiques urbanes. Cadascun d’aquests objectius es desenvolupa en cadascun dels articles científics que conformen aquesta tesi doctoral, publicats tots ells en revistes de revisió per parells. El primer article es proposa com a objectiu identificar els factors, relacionats amb el perfil socioeconòmic dels turistes i amb les característiques de la seva estada, que determinen la selecció d’opcions de transport i mobilitat sostenible per moure’s per la destinació urbana. El segon article pretén analitzar i comprendre com afecta el comportament espacio-temporal dels turistes en els seus patrons de consum econòmic i, per tant, en la generació d’ingressos per a l’economia local. El tercer article es proposa analitzar la influència de l’espai urbà sobre la forma en què els visitants es desplacen per la destinació. I finalment, el quart article té per objectiu reconstruir trajectòries i/o fluxos espacio-temporals a partir de dades geolocalitzades de les xarxes socials per tal de detectar patrons de mobilitat dels visitants de destinacions urbanes. Les fonts de dades i els mètodes utilitzats per complir amb els objectius de partida són diverses. En aquest sentit, la tesi aporta també una àmplia radiografia dels pros i les contres de les diferents fonts de dades disponibles per a l’anàlisi de les mobilitats dels visitants en destinacions turístiques.Esta tesis doctoral surge de la necesidad de profundizar en el conocimiento de las movilidades de los visitantes,entender las decisiones que configuran su comportamiento espaciotemporal e identificar y explorar los efectos que sus movilidades tienen sobre los destinos urbanos. La tesis se desarrolla en torno a cuatro objetivos específicos que se enmarcan en el ámbito de investigación de seguimiento de visitantes, y que se desarrollan en cada uno de los artículos científicos, publicados todos ellos en revistas de revisión por pares, que conforman esta tesis. El primer artículo se propone como objetivo identificar los factores, relacionados con el perfil socioeconómicos de los turistas y con las características de su estancia, que determinan la selección de opciones de transporte y movilidad sostenible para moverse por el destino urbano. El segundo artículo pretende analizar y comprender cómo afecta el comportamiento espaciotemporal de los turistas en sus patrones de consumo económico y, por tanto, en la generación de ingresos para la economía local. El tercer artículo se propone analizar la influencia del espacio urbano sobre la forma en que los visitantes se desplazan por el destino. Y finalmente, el cuarto artículo tiene por objetivo reconstruir trayectorias y / o flujos espaciotemporales a partir de datos geolocalizados de las redes sociales para detectar patrones de movilidad de los visitantes de destinos urbanos. Las fuentes de datos y los métodos utilizados para cumplir con los objetivos de partida son diversos. En este sentido, la tesis aporta también una amplia radiografía de los pros y contras de las diferentes fuentes de datos disponibles para el análisis de las movilidades de los visitantes en destinos turísticos.This dissertation arises from the need to deepen the knowledge of the mobility of visitors, understand the decisions that shape their spatiotemporal behaviour and identify and explore the effects that their mobility has on urban destinations. The thesis is developed around four specific objectives that fall within the scope of visitor tracking research, and that are developed in each of the scientific articles, all of them published in peer-reviewed journals, that make up this thesis. The first article aims to identify the factors, related to the socioeconomic profile of tourists and the characteristics of their stay, that determine the selection of sustainable transport and mobility options to move within the urban destination. The second article aims to analyse and understand how the visitors’ spatiotemporal behaviour affects their patterns of economic consumption and, therefore, the generation of income for the local economy. The third article aims to analyse the influence of the built environment on the visitors’ mobilities at destination. And finally, the fourth article aims to reconstruct trajectories and / or spatiotemporal flows from geolocated data obtained from social networks in order to detect visitors’ mobility patterns at urban destinations. The data sources and methods used to meet the objectives are multiple. In this sense, the thesis also provides an extensive x-ray of the pros and cons of the different data sources available for the analysis of visitors’ mobilities in tourist destinations

Pre-Crisis Determinants of Tourism Resilience

In this study, I provide a location choice model incorporating five pre-crisis determinants for tourists' destination choice to study the impact of these determinants on the resilience of tourism. The determinants are the kind of destination (rural vs. urban), the pre-crisis origin country specific attractiveness of the destination, the infrastructure size, the density, and the local culture. I estimate the effect of these determinants on the recovery of hotel overnight stays during the COVID-19 pandemic in Switzerland. I find that urban areas had an up to 75% percent lower recovery level. Half of this difference is due to their pre-crisis specific attractiveness for international tourists. This implies that a miss-match between long-run local touristic capital and domestic demand preferences prevents destinations from substituting the missing international tourists with domestic tourists. While infrastructure size affects the resilience ambiguously, density weakens the resilience in cities. Culture does not play a role if there are no local COVID-19 policies

Measuring destination image : a novel approach based on visual data mining. A methodological proposal and an application to European islands

Availability of User Generated Content and the development of Big Data and machine learning algorithms have paved the way to collecting and analysing great volumes of data. We scan imagery data from traveling-related posts on Instagram to identify the key features of the destination image and of its dynamics. Specifically, we exploit a newly introduced Visual Object Recognition tool (Google Cloud Vision) to convert into textual labels the content of about 860,000 travel-related pictures posted on Instagram in Summer 2019 for several European islands. The output, a vector of labels’ frequencies on a very fine-grained scale, is used to proxy the destination image at different points in time. We then introduce the Index of Distance in Destination Image, a metric built on the pictures’ labels ranking, and aimed at providing a quantitative measure of (dis)similarity between destination images. We show that the analysis of labels and the index are fit to compare destinations cross-sectionally and over time, providing a useful tool for researchers, marketers and DMOs. We also deliver evidence on how external shocks (like extreme events linked to climate change) or the organization of events modify the cognitive sphere of the destination image, with repercussions on activities undertaken by tourists and relevant implications for local policies