An intelligent destination recommendation system for tourists.

Choosing a tourist destination from the information available is one of the most complex tasks for tourists when making travel plans, both before and during their travel. With the development of a recommendation system, tourists can select, compare and make decisions almost instantly. This involves the construction of decision models, the ability to predict user preferences, and interpretation of the results. This research aims to develop a Destination Recommendation System (DRS) focusing on the study of machine-learning techniques to improve both technical and practical aspects in DRS. First, to design an effective DRS, an intensive literature review was carried out on published studies of recommendation systems in the tourism domain. Second, the thesis proposes a model-based DRS, involving a two-step filtering feature selection method to remove irrelevant and redundant features and a Decision Tree (DT) classifier to offer interpretability, transparency and efficiency to tourists when they make decisions. To support high scalability, the system is evaluated with a huge body of real-world data collected from a case-study city. Destination choice models were developed and evaluated. Experimental results show that our proposed model-based DRS achieves good performance and can provide personalised recommendations with regard to tourist destinations that are satisfactory to intended users of the system. Third, the thesis proposes an ensemble-based DRS using weight hybrid and cascade hybrid. Three classification algorithms, DT, Support Vector Machines (SVMs) and Multi- Layer Perceptrons (MLPs), were investigated. Experimental results show that the bagging ensemble of MLP classifiers achieved promising results, outperforming baseline learners and other combiners. Lastly, the thesis also proposes an Adaptive, Responsive, Interactive Model-based User Interface (ARIM-UI) for DRS that allows tourists to interact with the recommended results easily. The proposed interface provides adaptive, informative and responsive information to tourists and improves the level of the user experience of the proposed system

Deep learning and Internet of Things for tourist attraction recommendations in smart cities

The version of record is available online at: http://dx.doi.org/10.1007/s00521-021-06872-0We propose a tourist attraction IoT-enabled deep learning-based recommendation system to enhance tourist experience in a smart city. Travelers will enter details about their travels (traveling alone or with a companion, type of companion such as partner or family with kids, traveling for business or leisure, etc.) as well as user side information (age of the traveler/s, hobbies, etc.) into the smart city app/website. Our proposed deep learning-based recommendation system will process this personal set of input features to recommend the tourist activities/attractions that best fit his/her profile. Furthermore, when the tourists are in the smart city, content-based information (already visited attractions) and context-related information (location, weather, time of day, etc.) are obtained in real time using IoT devices; this information will allow our proposed deep learning-based tourist attraction recommendation system to suggest additional activities and/or attractions in real time. Our proposed multi-label deep learning classifier outperforms other models (decision tree, extra tree, k-nearest neighbor and random forest) and can successfully recommend tourist attractions for the first case [(a) searching for and planning activities before traveling] with the loss, accuracy, precision, recall and F1-score of 0.5%, 99.7%, 99.9%, 99.9% and 99.8%, respectively. It can also successfully recommend tourist attractions for the second case [(b) looking for activities within the smart city] with the loss, accuracy, precision, recall and F1-score of 3.7%, 99.5%, 99.8%, 99.7% and 99.8%, respectively.This work has been supported by the Agencia Estatal de Investigación of Spain under project PID2019-108713RB-C51/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Tour recommendation for groups

Consider a group of people who are visiting a major touristic city, such as NY, Paris, or Rome. It is reasonable to assume that each member of the group has his or her own interests or preferences about places to visit, which in general may differ from those of other members. Still, people almost always want to hang out together and so the following question naturally arises: What is the best tour that the group could perform together in the city? This problem underpins several challenges, ranging from understanding people’s expected attitudes towards potential points of interest, to modeling and providing good and viable solutions. Formulating this problem is challenging because of multiple competing objectives. For example, making the entire group as happy as possible in general conflicts with the objective that no member becomes disappointed. In this paper, we address the algorithmic implications of the above problem, by providing various formulations that take into account the overall group as well as the individual satisfaction and the length of the tour. We then study the computational complexity of these formulations, we provide effective and efficient practical algorithms, and, finally, we evaluate them on datasets constructed from real city data

Understanding tourist recommendation through destination image: A CHAID analysis

In spite of the efforts of marketers and managers to boost a favourable destination image, with a focus on encouraging tourists‟ revisit, other factors contribute to tourists‟ decision-making processes with regard to revisiting destinations. Moreover, recommendation from family and friends are considered to be the most credible source of information in the process of choosing a holiday destination, becoming relevant that studies on destination loyalty focus on this variable. Therefore, this research aims to identify the attributes which contribute to tourists‟ willingness to recommend a destination. The first stage of this study involved identifying the attributes to measure the image of Lagos in the Algarve region, an important Portuguese destination, through open-ended questions. In the second phase, the application of the Chi-Square Automatic Interaction Detector (CHAID) to survey responses from a sample of 379 tourists allowed to identify the features that explain the intention to recommend the destination. The results show that culture is the attribute with the strongest power to explain recommendation, highlighting the need for sun and sand tourism destinations to diversify their offer. Of the seven terminal nodes produced by CHAID, two segments with opposite trends were found, for which profiles were drawn

Destination image : perspectives of tourists versus residents

This study aims to measure the image of Lagos in the Algarve region, the most important Portuguese destination, in a cognitive, affective and behavioral approach. Given the lack of studies which compare the perspectives of tourists and residents, the empirical investigation includes a mixed methodology enabling a holistic approach followed by a quantitative methodology with the use of questionnaires. The attributes that are more consensually associated with Lagos are the good weather and good beaches, although these variables do not have significant discriminatory power for “recommendation of the destination to friends and family” as the dependent variable, in a CHAID analysis

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

Tourist trip planning functionalities : state-of-the-art and future

When tourists visit a city or region, they cannot visit every point of interest available, as they are constrained in time and budget. Tourist recommender applications help tourists by presenting a personal selection. Providing adequate tour scheduling support for these kinds of applications is a daunting task for the application developer. The objective of this paper is to demonstrate how existing models from the field of Operations Research (OR) fit this scheduling problem, and enable a wide range of tourist trip planning functionalities. Using the Orienteering Problem (OP) and its extensions to model the tourist trip planning problem, allows to deal with a vast number of practical planning problems

Mobile application to provide personalized sightseeing tours

Tourist recommendation systems have been growing over the last few years, mainly because of the use of mobile devices to obtain user context. This work discusses some of the most relevant systems on the field and presents PSiS Mobile, which is a mobile recommendation and planning application designed to support a tourist during his vacations. It provides recommendations about points of interest to visit based on tourist preferences and on user and sight context. Also, it suggests a visit planning which can be dynamically adapted based on current user and sight context. This tool works also like a journey dairy since it records the tourist moves and tasks to help him remember how the trip was like. To conclude, some field experiences will be presented.This work is part-funded by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by the National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects PSIS (PTDC/TRA /72152/2006), FCOMP-01- 0124 - FEDER-028980 (PTDC/EEI-SII/1386/2012) and PEst- OE / EEI / UI0752 / 2011

Visiting Time Prediction Using Machine Learning Regression Algorithm

Smart tourists cannot be separated with mobile technology. With the gadget, tourist can find information about the destination, or supporting information like transportation, hotel, weather and exchange rate. They need prediction of traveling and visiting time, to arrange their journey. If traveling time has predicted accurately by Google Map using the location feature, visiting time has another issue. Until today, Google detects the user’s position based on crowdsourcing data from customer visits to a specific location over the last several weeks. It cannot be denied that this method will give a valid information for the tourists. However, because it needs a lot of data, there are many destinations that have no information about visiting time. From the case study that we used, there are 626 destinations in East Java, Indonesia, and from that amount only 224 destinations or 35.78% has the visiting time. To complete the information and help tourists, this research developed the prediction model for visiting time. For the first data is tested statistically to make sure the model development was using the right method. Multiple linear regression become the common model, because there are six factors that influenced the visiting time, i.e. access, government, rating, number of reviews, number of pictures, and other information. Those factors become the independent variables to predict dependent variable or visiting time. From normality test as the linear regression requirement, the significant value was less than p that means the data cannot pass the statistic test, even though we transformed the data based on the skewness. Because of three of them are ordinal data and the others are interval data, we tried to exclude and include the ordinal by transform it to interval. We also used the Ordinal Logistic Regression by transform the interval data in dependent variable into ordinal data using Expectation Maximization, one of clustering algorithm in machine learning, but the model still did not fit even though we used 5 functions. Then we used the classification algorithm in machine learning by using 5 top algorithm which are Linear Regression, k-Nearest Neighbors, Decision Tree, Support Vector Machines, and Multi-Layer Perceptron. Based on maximum correlation coefficient and minimum root mean square error, Linear Regression with 6 independent variables has the best result with the correlation coefficient 20.41% and root mean square error 48.46%. We also compared with model using 3 independent variable, the best algorithm was still the same but with less performance. Then, the model was loaded to predict the visiting time for other 402 destinations

How would tourists use Green Spaces? Case Studies in Lisbon

EntretextosThis report provides in a relative condensed format the results of small-scale study undertaken in Lisbon during the Meeting of the CyberParks Project (www.cost.eu/COST_Actions/tud/Actions/TU1306). CyberParks is a COST Action coordinated by the Universidade Lusófona at the CeiED - Interdisciplinary Research Centre for Education and Development. The Project aims at creating a research platform on the relationship between Information and Communication Technologies (ICT) and the production of public open spaces, and their relevance to sustainable urban development. The impacts of this relationship are being explored from social, ecological, urban design and technological perspectives. Based on the supposition that the participants of the Meeting are tourists visiting Lisbon, a survey was carried out on the topic how people actually use and how they would use public spaces. This survey is also the first approach to the case study areas chosen in Lisbon: Parque Quinta das Conchas and Jardim da Estrela. Both green spaces will be subject of further studies in the forthcoming years. This study employed (1) a questionnaire for measuring the user’s experience and preferences, and (2) two different tracking devices that utilise GNSS (Global Navigation Satellite Systems), in our case the GPS for satellite positioning technologies. It also presents the results of a study on the relevance of wi-fi in Lisbon’s public spaces. Even considering that the surveys in Lisbon’s green spaces are a first exercise within the work programme of CyberParks they show important outcomes. On the one hand, regarding the technologies used and their potential for research and on the other hand the findings about Lisbon’s green spaces. It should be noted that the conducted surveys and the gathered data are statistically not representative, but can be characterised as an empirical case and as a showcase, as how tourists tend to use a green space. The results shows that surveys benefit from multiple research methods and from combining insights.Este relatório apresenta, em formato condensado, os resultados de um estudo de pequena escala realizado em Lisboa durante o Seminário do Projeto CyberParks. CyberParks é uma Ação COST coordenada pela Universidade Lusófona/CeiED - Centro de Estudos Interdisciplinares em Educação e Desenvolvimento. O projeto visa a criação de uma plataforma de debate sobre a relação entre as Tecnologias de Informação e Comunicação (TIC) e a produção de espaços públicos, e da sua relevância para o desenvolvimento urbano sustentável. Os impactos dessa relação estão a ser explorados a partir de perspetivas sociais, ecológicas, tecnológicas e de desenho urbano. Na sua etapa exploratória, este estudo assenta na suposição de que os participantes do Seminário são turistas de visita a Lisboa. A partir dos dados recolhidos pelos investigadores envolvidos na ação COST, foi realizada uma análise à forma como diferentes indivíduos usam, e como poderão usar, diferentes espaços públicos verdes. Este estudo apresenta, portanto, a primeira abordagem às áreas de estudos selecionadas em Lisboa. São elas o Parque Quinta das Conchas e o Jardim da Estrela. Ambos os espaços verdes serão objeto de novos estudos nos próximos anos. Neste primeiro estudo exploratório foram empregues: (1) um questionário, para aferir a experiência de um potencial utilizador e as suas preferências, e (2) dois dispositivos diferentes de rastreamento que utilizam tecnologia GNSS (Sistemas de Navegação Global por Satélite) e, no nosso caso, o GPS para as tecnologias de posicionamento por satélite. Ele também apresenta os resultados de um estudo realizado sobre a relevância do wi-fi em espaços públicos na cidade de Lisboa. Mesmo considerando que os estudos realizados nos espaços verdes representam um primeiro exercício no âmbito do programa de trabalho do CyberParks em Lisboa, são aqui revelados resultados importantes. Por um lado, o recurso às tecnologias utilizadas e seu potencial para a investigação e, por outro lado, os resultados sobre a vivência dos espaços verdes. Deve-se notar que os dados recolhidos não são estatisticamente representativos, mas evidenciam um caso empírico de como turistas tendem a usar um espaço verde urbano. A combinação do questionário com novos métodos digitais resultou num grande ganho de conhecimento, recobrindo as áreas de estudo sob a perspetiva de um turista, para além de maiores informações sobre as potencialidades e limites da tecnologia digital como ferramenta de investigação. Os resultados mostram que a investigação no campo social pode se beneficiar da combinação de vários métodos e técnicas