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

Intelligent Point-of-Interest Recommendation for Tourism Planning via Density-based Clustering and Genetic Algorithm

In recent years, geographic information service and relevant social media become more popular, some geographic point may interest people, e.g. scenic spot or famous store, naming as a point-of-interest (POI). However, the number of POI contributing by social media grows exponentially which causing a searching problem. How to recommend a POI to a user/tourist becomes a challenge. This study proposes an intelligent system using density-based clustering and genetic algorithm to recommend a POIs solution for tourism planning. Density-based clustering identifies candidate POIs. Skyline method decides a superior POI from candidate POIs by dominant of multiple attributes. Genetic algorithm optimizes the recommendation solution. The contribution is to get a tourism POI solution from a huge amount of candidate POIs based on user/tourist preferences. An experimental system implementation is in progress. In future, we will use open data from Google map and Foursquare to proof the proposed system mechanism effectiveness

Travel Package Recommendation

Location Based SocialNetworks (LBSN) benefit the users by allowing them to share their locations and life moments with their friends. The users can also review the locations they have visited. Classical recommender systems provide users a ranked list of single items. This is not suitable for applications like trip planning,where the recommendations should contain multiple items in an appropriate sequence. The problem of generating such recommendations is challenging due to various critical aspects, which includes user interest, budget constraints and high sparsity in the available data used to solve the problem. In this paper, we propose a graph based approach to recommend a set of personalized travel packages. Each recommended package comprises of a sequence of multiple Point of Interests (POIs). Given the current location and spatio-temporal constraints, our goal is to recommend a package which satisfies the constraints. This approach utilizes the data collected fromLBSNs to learn user preferences and also models the location popularity

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

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

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on theWeb such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.Project (the SmartSDK project is co-funded by the EU’s Horizon2020 programme under agreement number 723174 - c 2016 EC and the CONACYT’s agreement number 737373) Doctorado IndustrialAdministración y Dirección de EmpresasIngeniería, Industria y ConstrucciónTurism

Designing for Mixed Reality Urban Exploration

This paper introduces a design framework for mixed reality urban exploration (MRUE), based on a concrete implementation in a historical city. The framework integrates different modalities, such as virtual reality (VR), augmented reality (AR), and haptics-audio interfaces, as well as advanced features such as personalized recommendations, social exploration, and itinerary management. It permits to address a number of concerns regarding information overload, safety, and quality of the experience, which are not sufficiently tackled in traditional non-integrated approaches. This study presents an integrated mobile platform built on top of this framework and reflects on the lessons learned

Designing for Mixed Reality Urban Exploration

This paper introduces a design framework for mixed reality urban exploration (MRUE), based on a concrete implementation in a historical city. The framework integrates different modalities, such as virtual reality (VR), augmented reality (AR), and haptics-audio interfaces, as well as advanced features such as personalized recommendations, social exploration, and itinerary management. It permits to address a number of concerns regarding information overload, safety, and quality of the experience, which are not sufficiently tackled in traditional non-integrated approaches. This study presents an integrated mobile platform built on top of this framework and reflects on the lessons learned.Peer reviewe