Do people communicate about their whereabouts? Investigating the relation between user-generated text messages and Foursquare check-in places

The social functionality of places (e.g. school, restaurant) partly determines human behaviors and reflects a region’s functional configuration. Semantic descriptions of places are thus valuable to a range of studies of humans and geographic spaces. Assuming their potential impacts on human verbalization behaviors, one possibility is to link the functions of places to verbal representations such as users’ postings in location-based social networks (LBSNs). In this study, we examine whether the heterogeneous user-generated text snippets found in LBSNs reliably reflect the semantic concepts attached with check-in places. We investigate Foursquare because its available categorization hierarchy provides rich a-priori semantic knowledge about its check-in places, which enables a reliable verification of the semantic concepts identified from user-generated text snippets. A latent semantic analysis is conducted on a large Foursquare check-in dataset. The results confirm that attached text messages can represent semantic concepts by demonstrating their large correspondence to the official Foursquare venue categorization. To further elaborate the representativeness of text messages, this work also performs an investigation on the textual terms to quantify their abilities of representing semantic concepts (i.e., representativeness), and another investigation on semantic concepts to quantify how well they can be represented by text messages (i.e., representability). The results shed light on featured terms with strong locational characteristics, as well as on distinctive semantic concepts with potentially strong impacts on human verbalizations

Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications throughout the world, the amount of data stored on the Web has been growing exponentially. In this new digital era, a large number of companies have emerged with the purpose of ltering the information available on the web and provide users with interesting items. The algorithms and models used to recommend these items are called Recommender Systems. These systems are applied to a large number of domains, from music, books, or movies to dating or Point-of-Interest (POI), which is an increasingly popular domain where users receive recommendations of di erent places when they arrive to a city. In this thesis, we focus on exploiting the use of contextual information, especially temporal and sequential data, and apply it in novel ways in both traditional and Point-of-Interest recommendation. We believe that this type of information can be used not only for creating new recommendation models but also for developing new metrics for analyzing the quality of these recommendations. In one of our rst contributions we propose di erent metrics, some of them derived from previously existing frameworks, using this contextual information. Besides, we also propose an intuitive algorithm that is able to provide recommendations to a target user by exploiting the last common interactions with other similar users of the system. At the same time, we conduct a comprehensive review of the algorithms that have been proposed in the area of POI recommendation between 2011 and 2019, identifying the common characteristics and methodologies used. Once this classi cation of the algorithms proposed to date is completed, we design a mechanism to recommend complete routes (not only independent POIs) to users, making use of reranking techniques. In addition, due to the great di culty of making recommendations in the POI domain, we propose the use of data aggregation techniques to use information from di erent cities to generate POI recommendations in a given target city. In the experimental work we present our approaches on di erent datasets belonging to both classical and POI recommendation. The results obtained in these experiments con rm the usefulness of our recommendation proposals, in terms of ranking accuracy and other dimensions like novelty, diversity, and coverage, and the appropriateness of our metrics for analyzing temporal information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones en todo el mundo, la cantidad de datos almacenados en la red ha crecido exponencialmente. En esta nueva era digital, han surgido un gran n umero de empresas con el objetivo de ltrar la informaci on disponible en la red y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos utilizados para recomendar estos art culos reciben el nombre de Sistemas de Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios, desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs, en ingl es), un dominio cada vez m as popular en el que los usuarios reciben recomendaciones de diferentes lugares cuando llegan a una ciudad. En esta tesis, nos centramos en explotar el uso de la informaci on contextual, especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa tanto en la recomendaci on cl asica como en la recomendaci on de POIs. Creemos que este tipo de informaci on puede utilizarse no s olo para crear nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas m etricas para analizar la calidad de estas recomendaciones. En una de nuestras primeras contribuciones proponemos diferentes m etricas, algunas derivadas de formulaciones previamente existentes, utilizando esta informaci on contextual. Adem as, proponemos un algoritmo intuitivo que es capaz de proporcionar recomendaciones a un usuario objetivo explotando las ultimas interacciones comunes con otros usuarios similares del sistema. Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y 2019, identi cando las caracter sticas comunes y las metodolog as utilizadas. Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking. Adem as, debido a la gran di cultad de realizar recomendaciones en el ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos para utilizar la informaci on de diferentes ciudades y generar recomendaciones de POIs en una determinada ciudad objetivo. En el trabajo experimental presentamos nuestros m etodos en diferentes conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los resultados obtenidos en estos experimentos con rman la utilidad de nuestras propuestas de recomendaci on en t erminos de precisi on de ranking y de otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de apropiadas son nuestras m etricas para analizar la informaci on temporal y los sesgos en las recomendaciones producida

Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks

[EN] Promoting a tourist destination requires uncovering travel patterns and destination choices, identifying the profile of visitors and analyzing attitudes and preferences of visitors for the city. To this end, tourism-related data are an invaluable asset to understand tourism behaviour, obtain statistical records and support decision-making for business around tourism. In this work, we study the behaviour of tourists visiting top attractions of a city in relation to the tourist influx to restaurants around the attractions. We propose to undertake this analysis by retrieving information posted by visitors in a social network and using an open access map service to locate the tweets in a influence area of the city. Additionally, we present a pattern recognition based technique to differentiate visitors and locals from the collected data from the social network. We apply our study to the city of Valencia in Spain and Berlin in Germany. The results show that, while in Valencia the most frequented restaurants are located near top attractions of the city, in Berlin, it is usually the case that the most visited restaurants are far away from the relevant attractions of the city. The conclusions from this study can be very insightful for destination marketers.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Bustamante, A.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2019). Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks. Sensors. 19(11):1-25. https://doi.org/10.3390/s19112612S1251911Travel and Tourism Competitiveness Report 2017http://reports.weforum.org/travel-and-tourism-competitiveness-report-2017/OECD Datahttps://data.oecd.org/Travel &Tourism: Economic Impact 2019 Worldhttps://www.wttc.org/-/media/files/reports/economic-impact-research/regions-2019/world2019.pdfCohen, S. A., Prayag, G., & Moital, M. (2013). Consumer behaviour in tourism: Concepts, influences and opportunities. Current Issues in Tourism, 17(10), 872-909. doi:10.1080/13683500.2013.850064Yoo, C.-K., Yoon, D., & Park, E. (2018). Tourist motivation: an integral approach to destination choices. Tourism Review, 73(2), 169-185. doi:10.1108/tr-04-2017-0085Cohen, E. (1979). A Phenomenology of Tourist Experiences. Sociology, 13(2), 179-201. doi:10.1177/003803857901300203Decrop, A., & Snelders, D. (2005). A grounded typology of vacation decision-making. Tourism Management, 26(2), 121-132. doi:10.1016/j.tourman.2003.11.011Servidio, R., & Ruffolo, I. (2016). Exploring the relationship between emotions and memorable tourism experiences through narratives. Tourism Management Perspectives, 20, 151-160. doi:10.1016/j.tmp.2016.07.010Prayag, G., Hosany, S., Muskat, B., & Del Chiappa, G. (2016). Understanding the Relationships between Tourists’ Emotional Experiences, Perceived Overall Image, Satisfaction, and Intention to Recommend. Journal of Travel Research, 56(1), 41-54. doi:10.1177/0047287515620567Valls, J.-F., Sureda, J., & Valls-Tuñon, G. (2014). Attractiveness Analysis of European Tourist Cities. Journal of Travel & Tourism Marketing, 31(2), 178-194. doi:10.1080/10548408.2014.873310García-Palomares, J. C., Gutiérrez, J., & Mínguez, C. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408-417. doi:10.1016/j.apgeog.2015.08.002Lu, Y., Wu, H., Liu, X., & Chen, P. (2019). TourSense: A Framework for Tourist Identification and Analytics Using Transport Data. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2407-2422. doi:10.1109/tkde.2019.2894131Buhalis, D. (2000). Marketing the competitive destination of the future. Tourism Management, 21(1), 97-116. doi:10.1016/s0261-5177(99)00095-3Indicators for Measuring Competitiveness in Tourism: A Guidance Documenthttp://dx.doi.org/10.1787/5k47t9q2t923-enLonghi, C., Titz, J.-B., & Viallis, L. (2014). Open Data: Challenges and Opportunities for the Tourism Industry. Tourism Management, Marketing, and Development, 57-76. doi:10.1057/9781137354358_4Open Data in Tourismhttps://www.europeandataportal.eu/en/highlights/open-data-tourismCox, C., Burgess, S., Sellitto, C., & Buultjens, J. (2009). The Role of User-Generated Content in Tourists’ Travel Planning Behavior. Journal of Hospitality Marketing & Management, 18(8), 743-764. doi:10.1080/19368620903235753Lu, W., & Stepchenkova, S. (2014). User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software. Journal of Hospitality Marketing & Management, 24(2), 119-154. doi:10.1080/19368623.2014.907758Pantano, E., Priporas, C.-V., & Stylos, N. (2017). ‘You will like it!’ using open data to predict tourists’ response to a tourist attraction. Tourism Management, 60, 430-438. doi:10.1016/j.tourman.2016.12.020Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260-271. doi:10.1080/15230406.2014.890072Girardin, F., Calabrese, F., Fiore, F. D., Ratti, C., & Blat, J. (2008). Digital Footprinting: Uncovering Tourists with User-Generated Content. IEEE Pervasive Computing, 7(4), 36-43. doi:10.1109/mprv.2008.71Alivand, M., & Hochmair, H. H. (2016). Spatiotemporal analysis of photo contribution patterns to Panoramio and Flickr. Cartography and Geographic Information Science, 44(2), 170-184. doi:10.1080/15230406.2016.1211489Bassolas, A., Lenormand, M., Tugores, A., Gonçalves, B., & Ramasco, J. J. (2016). Touristic site attractiveness seen through Twitter. EPJ Data Science, 5(1). doi:10.1140/epjds/s13688-016-0073-5Mariani, 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-07-2017-0461Francalanci, C., & Hussain, A. (2015). Discovering social influencers with network visualization: evidence from the tourism domain. Information Technology & Tourism, 16(1), 103-125. doi:10.1007/s40558-015-0030-3Williams, N. L., Inversini, A., Ferdinand, N., & Buhalis, D. (2017). Destination eWOM: A macro and meso network approach? Annals of Tourism Research, 64, 87-101. doi:10.1016/j.annals.2017.02.007Salas-Olmedo, M. H., Moya-Gómez, B., García-Palomares, J. C., & Gutiérrez, J. (2018). Tourists’ digital footprint in cities: Comparing Big Data sources. Tourism Management, 66, 13-25. doi:10.1016/j.tourman.2017.11.001Padilla, J. J., Kavak, H., Lynch, C. J., Gore, R. J., & Diallo, S. Y. (2018). Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter. PLOS ONE, 13(6), e0198857. doi:10.1371/journal.pone.0198857Maeda, T., Yoshida, M., Toriumi, F., & Ohashi, H. (2018). Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data. ISPRS International Journal of Geo-Information, 7(3), 99. doi:10.3390/ijgi7030099Wöber, 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)00071-7Sabou, M., Onder, I., Brasoveanu, A. M. P., & Scharl, A. (2016). Towards cross-domain data analytics in tourism: a linked data based approach. Information Technology & Tourism, 16(1), 71-101. doi:10.1007/s40558-015-0049-5Adamiak, C., Szyda, B., Dubownik, A., & García-Álvarez, D. (2019). Airbnb Offer in Spain—Spatial Analysis of the Pattern and Determinants of Its Distribution. ISPRS International Journal of Geo-Information, 8(3), 155. doi:10.3390/ijgi8030155Padron Municipal de Habitantes [Statistical Report: Residents in Valencia in 2018]https://bit.ly/2JnNNE