An Ontology-based Approach for Personalized Itinerary Search

Personalization plays an important role in information retrieval systems. In the field of transportation, and more specifically multimodal transportation, personalization represents an efficient way for travelers to find appropriate routes. Providing travelers with the relevant information to their needs and preferences is challenging for transportation systems. In this paper, we propose an ontology-based approach for personalized itinerary search. Our proposal is based on modeling each user using an ontological fuzzy modular profile that incorporates a set of fuzzy modules representing several aspects of the user’s description. The approach is applied in the transportation domain and integrates a new method of matching between the profile ontology and the domain ontology to obtain personalized responses for individual user profiles. Our proposal was implemented and evaluated. Obtained results show that personalization coupled with ontology matching enables an improvement of query reformulation

Using routes or itineraries to create networks in regions with low competitiveness

There are territories where the ability of territorial competitiveness can be conditioned by the availability of resources, access, and capacity to attract and retain tourists. The barrier of competitiveness of territories, especially desertified territories with scarce resources, can be overcome through the involvement of all and the integration of stakeholders in a collaborative network. In this sense, with the aim of structuring the offer of tourist routes and itineraries according to the needs of the demand, based on the available resources, it is proposed to structure three models of itineraries according to demand, which can be stated as follows: i) have a reduced cost through a standardized itinerary; ii) be directed towards a group of customers through a segmented itinerary; or iii) make the offer as flexible as possible to meet the specific needs, desires or expectations of a tourist, through a customized itinerary. This paper uses action research to contribute to the improvement of the functioning of routes and itineraries in low-density territories.info:eu-repo/semantics/draf

Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization Proposal

In recent years, recommender systems have been used as a solution to support tourists with recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD) – Orienteering Problem (OP) – Time Windows (TW), which analyzes in real time the user’s constraints and the points of interest’s constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm. Then, with a genetic algorithm (GA), we optimize the proposed itineraries to tourists for facilitating the organization of their visits. We also used a parametrized fitness function to include any element of the context to generate an optimized recommendation. Our recommender is different from others because it is scalable and adaptable to environmental changes and users’ interests, and it offers real-time recommendations. To test our recommender, we developed an application that uses our algorithm. Finally, 131 tourists used this recommender system and an analysis of users’ perceptions was developed. Metrics were also used to detect the percentage of precision, in order to determine the degree of accuracy of the recommender system. This study has implications for researchers interested in developing software to recommend the best itinerary for tourists with constraint controls with regard to the optimized itineraries

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

A communication plan for a portuguese travel agency: Portugal with

Portugal With is a Portuguese travel agency founded in 2013 with the aim of providing a personalized and personal experience to its customers. It offers a wide range of trips with the support of several national and international operators. Taking into account the family origin of this company, spreading the word, has always been the main way of publicizing the brand. However, today's digital age and the pandemic situation that has existed for over a year have created a need to invest more in marketing, essentially digital, to increase brand awareness and intensify its presence with the target audience. In this way, a Communication Plan for the second half of 2021 was created in order to study the characteristics and behavior of the consumer in relation to the travel agency market and, thus, assist Portugal With, with a strategy that will achieve the above objectives mentioned. To develop this communication plan, the author first made use of a literature review, external analysis and internal analysis. In addition, she conducted an online survey and an interview with the person responsible for Portugal With, who provided a clear view of the brand and the characteristics of the target audience. As a result of this initial approach, the proposed Communication Plan resulted in a strategy that allows reaching the objectives defined with communication actions in order to improve the current promotional mix.A Portugal With é uma agência de viagens portuguesa fundada em 2013 com o objetivo de proporcionar uma experiência personalizada e pessoal aos seus clientes. Disponibiliza uma vasta oferta de viagens, contando com o apoio de vários operadores nacionais e internacionais. Tendo em conta a origem familiar desta empresa, o passa-palavra foi desde sempre o principal meio de divulgação da marca. No entanto, a era digital dos dias de hoje e a situação pandémica existente há mais de um ano, criaram uma necessidade de investir mais em marketing, essencialmente digital, para aumentar o reconhecimento da marca e intensificar a presença da mesma junto do público-alvo. Desta forma, foi criado um Plano de Comunicação para o segundo semestre de 2021, com o intuito de estudar as caraterísticas e comportamento do consumidor relativamente ao mercado das agências de viagens e desta forma, auxiliar a Portugal With com uma estratégia que permita atingir os objetivos acima mencionados. Para desenvolver este plano de comunicação, a autora recorreu primeiramente a uma revisão de literatura, análise externa e análise interna. Para além disso, realizou um questionário online e uma entrevista com a responsável pela Portugal With, que forneceram uma visão clara sobre a marca e as caraterísticas do público-alvo. Como consequência desta abordagem inicial resultou o Plano de Comunicação proposto em que se definiu uma estratégia que permite alcançar os objetivos definidos com ações de comunicação, de forma a melhorar o mix promocional atual

Information technology management system: an analysis on computational model failures for fleet management

This article proposes an information technology model to evaluate fleet management failure. Qualitative research done by a case study within an Interstate  Transport company in a São Paulo State proposed to establish a relationship between computer tools and valid trustworthy information needs, and within an acceptable timeframe, for decision making, reliability, availability and system management. Additionally, the study aimed to provide relevant and precise information, in order to minimize and mitigate failure actions that may occur, compromising all operational organization base functioning.Este artigo propõe um modelo de tecnologia de informação para avaliação de falhas na gestão de frotas. A pesquisa qualitativa realizada por um estudo de caso numa empresa de Transporte Rodoviário Interestadual no Estado de São Paulo, propôs estabelecer relações entre as ferramentas computacionais e a necessidade de informações, fidedignas e em intervalos de tempo aceitáveis como válidas, para as tomadas de decisão, confiabilidade, disponibilidade e gestão de sistemas. Adicionalmente, o estudo visou fornecer informações relevantes e precisas, de forma a minimizar e mitigar ações de falhas que possam ocorrer, comprometendo o funcionamento de toda a base operacional da organização

Fashion Industry

Fashion is a lot more than providing an answer to primary needs. It is a way of communication, of distinction, of proclaiming a unique taste and expressing the belonging to a group. Sometimes to an exclusive group. Currently, the fashion industry is moving towards hyperspace, to a multidimensional world that is springing from the integration of smart textiles and wearable technologies. It is far beyond aesthetics. New properties of smart textiles let designers experiment with astonishing forms and expressions. There are also surprising contrasts and challenges: a new life for natural fibers, sustainable fabrics and dyeing techniques, rediscovered by eco-fashion, and "artificial apparel," made of wearable electronic components. How is this revolution affecting the strategies of the fashion industry

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