A mobile tour guide app for sustainable tourism

Portugal has had a flourishing tourism sector for the past few years. In fact, Portugal’s tourism boom has made the industry one of the biggest contributors to the national economy and the largest employer. In the year 2019, Portugal had a total of 27 million tourists, surpassing once again the record established in the previous year. However, tourism also brings a series of unintended negative side effects, such as overcrowding. The Santa Maria Maior historic district in Lisbon is being particularly affected by this problem. The work undertaken in this dissertation is part of the Sustainable Tourism Crowding project, that aims to mitigate the overcrowding phenomenon in this district, by fostering a balanced distribution of visitors while promoting the visitation of sustainable points of interest. This dissertation focuses on developing a mobile app prototype targeted at tourists, through which these sustainable walking tour recommendations can be delivered. To validate the functional requirements of the prototype, more specifically the trip creation process, a series of unit tests, integration tests, and manual tests were developed. To evaluate the usability of the prototype, a user-centered approach was adopted during the design stage, in which two usability techniques were conducted with members of ISCTE’s research center ISTAR and partners from the Junta de Freguesia de Santa Maria Maior, that guided and validated the decisions made. The achieved prototype contains mechanisms for measuring tourists’ adherence to the recommended tours using the Dynamic Time Warping algorithm, which raises new research opportunities on tourists’ behaviour.O desenvolvimento próspero do setor turístico em Portugal nos últimos anos fez da indústria um dos maiores contribuintes para a economia nacional e o maior empregador do país. No ano de 2019, Portugal recebeu um total de 27 milhões de turistas, ultrapassando uma vez mais uma vez o recorde estabelecido no ano anterior. No entanto, o turismo traz também uma série de efeitos secundários negativos não intencionais, tais como overcrowding. A freguesia histórica de Santa Maria Maior em Lisboa está a ser particularmente afetada por este problema. O trabalho desenvolvido nesta dissertação faz parte do projeto de pesquisa Sustainable Tourism Crowding, que visa mitigar o fenómeno de overcrowding nesta freguesia, promovendo uma distribuição equilibrada dos visitantes e incentivando a visita de pontos de interesse sustentáveis. Esta dissertação foca-se no desenvolvimento de uma aplicação móvel protótipo destinada a turistas, através do qual recebem recomendações de visitas sustentáveis. Para validar os requisitos funcionais do protótipo, mais especificamente o processo de criação de visitas, foram desenvolvidos testes unitários, testes de integração, e testes manuais. Para avaliar a usabilidade do protótipo, foi adotada uma abordagem centrada no utilizador durante a fase de conceção, em que foram utilizadas duas técnicas de usabilidade em parceria com o ISTAR (centro de investigação do ISCTE) e com a Junta de Freguesia de Santa Maria Maior, cujos resultados guiaram e validaram as decisões tomadas. O protótipo desenvolvido contém mecanismos para medir a aderência dos turistas às recomendações sugeridas através do algoritmo Dynamic Time Warping, proporcionando novas oportunidades de pesquisa nesta área

A context aware recommender system for tourism with ambient intelligence

Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO O sistema de recomendação (RS) detém um lugar significativo na área do sector do turismo. O principal fator do planeamento de viagens é selecionar pontos de interesse relevantes (PoI) do domínio do turismo. O sistema de recomendação (SR) deve recolher informações de comportamentos, personalidade, preferências e outras informações contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferências do utilizador e na análise de traços fisiológicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restrições (CSP). Propõe-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turístico possível para o utilizador final. A principal contribuição deste SR é representar o modelo como CSP e tratá-lo como problema de otimização. Implementámos o nosso sistema com o solucionador em Minizinc com restrições de domínio representadas pelas preferências dos utilizadores. O CSP permitiu que as preferências dos utilizadores guiassem o sistema para encontrar as soluções ideais

Enhancing travel experience with the combination of information visualization, situation awareness, and distributed cognition

With the new forms of travel introduced by new technologies of transportation and communication, a satisfied travel experience could be affected by various factors before and during a trip. Especially for road trips, traveling by car provides freedom on time control while leading to more possibilities of rescheduling initial plans made under time constraints. When overwhelmed with the need for changed travel context to avoid unexpected events that will require a serious change of initial plans, travelers need to find and access helpful contextual information quickly. This is a context-related decision making process that requires amplifying human situation awareness and supporting distributed cognition, since travel information offers multiple choices. To solve this problem, I applied information visualization as the main design solution. When comparing it with a traditional representation of lists, information visualization displays the advantages of visual representation of abstract data to clarify and depict the information and amplify cognition while improving travel experience intuitively in the domain of user experience design. Therefore in this thesis I will address the approach of implementing recontextualized situation awareness, distributed cognition, and information visualization in a travel-aid system. By using both theoretical and practical design perspectives, I will discuss how to enhance travel experience with represented contextual information that users desire or expect before and during a road trip. I will also explore the new values of this design with strategic business support. Additionally, after conducting research and analysis on existing interaction design parts, I selected a smartphone app to serve as a proper platform with connected multifunctions. Briefly, I begin the thesis with a review of previous theories and aspects of travel planning, information visualization as it relates to travel, situation awareness, and distributed cognition in the design context and related smartphone apps. Then I discuss the process of identifying the specific issues to be solved or improved with a preliminary research of empirical study, followed by an interview, online survey, insights synthesis, and business model design. After a visual-system design was developed, heuristic evaluation was employed to assess the outcome. Lastly, a new round of refined design results is introduced based on outcomes of the evaluation

VoyageWithUs : a recommender platform that enhances group travel planning

Group travel planning poses unique challenges such as choosing hotels, restaurants and venues while catering to everyone’s wants and needs, or sharing trip itineraries and artifacts among trip participants. State of the art travel planning applications such as Yelp and TripAdvisor, while integrating with social networks and making recommendations, don’t offer recommendations for specific groups of travelers. On the other hand, while TripCase offers trip planning capabilities and email sharing, it doesn’t offer a full interactive travel planner that allows groups to contribute to the travel planning process. This report proposes an approach to making personalized group travel recommendations based on hybrid recommendation techniques that aggregates individual recommendations to find common ground between trip participants. This is achieved by designing a recommender system that uses data from a location based social network(LBSN) and makes recommendations based on the trip location, then refines them by applying incremental filters which are responsible for incorporating user preferences, similarity to other users and user context. Finally, it takes the generated recommendations for each trip participant and ranks them such that the items most highly ranked are the ones most likely to fit everyone’s preferences. The rationale for choosing a hybrid recommender system is to address common issues such as the cold start problem, where the quality of the recommendations is affected by either too few reviewers for a certain point of interest(POI) or too few reviews generated by trip participants. These issues, along with a coverage of related work is detailed in the first part of this report. In order to make the applicability of the recommender more tangible, I integrated it into a proof of concept mobile application that also allows travelers to collaborate and share travel planning artifacts, and generates itineraries based on the recommendations made. The recommender accuracy was measured against recommendations made by state of the art applications, while individual filters were evaluated using commonly used metrics. The recommender was tested in a series of relevant scenarios proving the effectiveness of the approach in making group travel recommendations, versus individual recommendations generated by other applications.Electrical and Computer Engineerin

Marketing of Tourism Destination in the Context of Tiger Safari

Tiger tourism plays a significant role in the overall scenario of Indian tourism. The forest destination managers face a major challenge in satisfying their visitors since tigers are elusive by nature and most of the time tourists return dissatisfied without sighting a tiger after a forest safari. This paper is the first scientific study of its kind based on empirical data in the context of tiger tourism and proposed a model to identify the optimum path in the forest with a higher probability of tiger sighting

UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations

Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user satisfaction with the platform. However, the implementation of these systems largely depends on the context, which can vary from recommending an item or package to a user or a group. This requires careful exploration of several models during the deployment, as there is no comprehensive and unified approach that deals with recommendations at different levels. Furthermore, these individual models must be closely attuned to their generated recommendations depending on the context to prevent significant variation in their generated recommendations. In this paper, we propose a novel unified recommendation framework that addresses all four recommendation tasks, namely personalized, group, package, or package-to-group recommendation, filling the gap in the current research landscape. The proposed framework can be integrated with most of the traditional matrix factorization-based collaborative filtering models. The idea is to enhance the formulation of the existing approaches by incorporating components focusing on the exploitation of the group and package latent factors. These components also help in exploiting a rich latent representation of the user/item by enforcing them to align closely with their corresponding group/package representation. We consider two prominent CF techniques, Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks. Experiment results on two publicly available datasets are reported, comparing them to other baseline approaches that consider individual rating feedback for group or package recommendations.Comment: 25 page

Consumer Acceptance of Recommendations by Interactive Decision Aids: The Joint Role of Temporal Distance and Concrete vs. Abstract Communications

Interactive decision aids (IDAs) typically use concrete product feature-based approaches to interact with consumers. Recently however, interaction designs that focus on communicating abstract consumer needs have been suggested as a promising alternative. This article investigates how temporal distance moderates the effectiveness of these two competing IDA communication designs by its effect on consumers’ mental representation of the product decision problem. Temporal distance is inherently connected to IDAs in two ways. Congruency between consumption timing (immediate vs. distant) and IDA communication design (concrete vs. abstract, respectively) increases the likelihood to accept the IDA’s advice. This effect is also achieved by congruency between IDA process timing (immediate vs. delayed delivery of recommendations) and IDA communication design (concrete vs. abstract, respectively). We further show that this process is mediated by the perceived transparency of the IDA process. Managers and researchers need to take into account the importance of congruency between the user and the interface through which companies interact with their users and can further optimize IDAs so that they better match consumers’ mental representations

Enhancing travel recommendations: Ai-driven personalization through user digital footprints

Esta tesis tiene como objetivo examinar la manera en que la huella digital que dejan los usuarios en internet puede utilizarse para optimizar la personalización de los servicios turísticos, mediante el uso de inteligencia artificial. El documento propone que el auge de la inteligencia artificial ha abierto un mundo de oportunidades para desarrollar nuevas herramientas para mejorar la experiencia de viaje digital. El enfoque se basa en la idea de que las huellas digitales son únicas y particulares de cada individuo y estos valiosos datos pueden dar lugar a sugerencias de viaje más inteligentes y certeras. Se consideran las actitudes de comportamiento del usuario, como la influencia del contenido generado por el usuario en las redes sociales y el boca a boca electrónico en el proceso de planificación del viaje, así como las implicaciones de este rastro de datos en la optimización de los servicios de viaje personalizados. Este modelo describe la relación entre la inteligencia artificial y la hiper personalización de servicios. Como es una tendencia creciente que está alterando nuestra realidad actual, la tesis presentada desarrolla una aplicación de viajes a medida que, con el permiso del usuario, aprovecha los datos recopilados de las redes sociales personales para construir un plan de viaje específico basado en las preferencias individuales.This thesis aims to examine the way the digital footprint users leave behind can be utilized to optimize the personalization of tourism services, through the use of artificial intelligence. The paper proposes that the surge of artificial intelligence has opened a world of opportunities to develop new tools to improve the digital travel experience. The approach is based on the idea that digital footprints are unique and particular to each individual and this valuable data can result in smarter and unerring travel suggestions. Behavioral attitudes of the user, such as the influence of user-generated content in social media and e-word of mouth in the travel planning process, are considered, as well as the implications of this data trail in the optimization of customized travel services. This model describes the relationship between artificial intelligence and hyper-personalization of services. As it is a growing trend that is disrupting our current reality, the presented thesis develops a tailor-made traveling application that, with permission of the user, leverages the data collected from personal social media to build a specific travel plan based on each user’s preferences