Recommendation and planning through mobile devices in tourism context

In this paper we present a mobile recommendation and planning system, named PSiS Mobile. It is designed to provide effective support during a tourist visit through context-aware information and recommendations about points of interest, exploiting tourist preferences and context. Designing a tool like this brings several challenges that must be addressed. We discuss how these challenges have been overcame, present the overall system architecture, since this mobile application extends the PSiS project website, and the mobile application architecture.

Tourism mobile and recommendation systems - a state of the art

Recommendation systems have been growing in number for the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages.

A hybrid recommendation approach for a tourism system

Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.

Recommendation & mobile systems - a state of the art for tourism

Recommendation systems have been growing in number over the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. These approaches contain strengths and weaknesses that need to be evaluated according to the knowledge area in which the system is going to be implemented. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages.

Lightweight user modeling – a case study

In the context of previous publications, we propose a new lightweight UM process, intended to work as a tourism recommender system in a commercial environment. The new process tackles issues like cold start, gray sheep and over specialization through a rich user model and the application of a gradual forgetting function to the collected user action history. Also, significant performance improvements were achieved regarding the previously proposed UM process.

User modeling and recommendation strategies for tourism

Mestrado em Engenharia Informática

Citrate selective electrodes for the flow injection analysis of soft drinks, beers and pharmaceutical products

Aiming the establishment of simple and accurate readings of citric acid (CA) in complex samples, citrate (CIT) selective electrodes with tubular configuration and polymeric membranes plus a quaternary ammonium ion exchanger were constructed. Several selective membranes were prepared for this purpose, having distinct mediator solvents (with quite different polarities) and, in some cases, p-tert-octylphenol (TOP) as additive. The latter was used regarding a possible increase in selectivity. The general working characteristics of all prepared electrodes were evaluated in a low dispersion flow injection analysis (FIA) manifold by injecting 500µl of citrate standard solutions into an ionic strength (IS) adjuster carrier (10−2 mol l−1) flowing at 3ml min−1. Good potentiometric response, with an average slope and a repeatability of 61.9mV per decade and ±0.8%, respectively, resulted from selective membranes comprising additive and bis(2-ethylhexyl)sebacate (bEHS) as mediator solvent. The same membranes conducted as well to the best selectivity characteristics, assessed by the separated solutions method and for several chemical species, such as chloride, nitrate, ascorbate, glucose, fructose and sucrose. Pharmaceutical preparations, soft drinks and beers were analyzed under conditions that enabled simultaneous pH and ionic strength adjustment (pH = 3.2; ionic strength = 10−2 mol l−1), and the attained results agreed well with the used reference method (relative error < 4%). The above experimental conditions promoted a significant increase in sensitivity of the potentiometric response, with a supra-Nernstian slope of 80.2mV per decade, and allowed the analysis of about 90 samples per hour, with a relative standard deviation <1.0%.

Convex optimization framework for intermediate deadline assignment in soft and hard real-time distributed systems

It is generally challenging to determine end-to-end delays of applications for maximizing the aggregate system utility subject to timing constraints. Many practical approaches suggest the use of intermediate deadline of tasks in order to control and upper-bound their end-to-end delays. This paper proposes a unified framework for different time-sensitive, global optimization problems, and solves them in a distributed manner using Lagrangian duality. The framework uses global viewpoints to assign intermediate deadlines, taking resource contention among tasks into consideration. For soft real-time tasks, the proposed framework effectively addresses the deadline assignment problem while maximizing the aggregate quality of service. For hard real-time tasks, we show that existing heuristic solutions to the deadline assignment problem can be incorporated into the proposed framework, enriching their mathematical interpretation.

Online robust optimization framework for QoS guarantees in distributed soft real-time systems

In distributed soft real-time systems, maximizing the aggregate quality-of-service (QoS) is a typical system-wide goal, and addressing the problem through distributed optimization is challenging. Subtasks are subject to unpredictable failures in many practical environments, and this makes the problem much harder. In this paper, we present a robust optimization framework for maximizing the aggregate QoS in the presence of random failures. We introduce the notion of K-failure to bound the effect of random failures on schedulability. Using this notion we define the concept of K-robustness that quantifies the degree of robustness on QoS guarantee in a probabilistic sense. The parameter K helps to tradeoff achievable QoS versus robustness. The proposed robust framework produces optimal solutions through distributed computations on the basis of Lagrangian duality, and we present some implementation techniques. Our simulation results show that the proposed framework can probabilistically guarantee sub-optimal QoS which remains feasible even in the presence of random failures.

Assessing and comparing the total antioxidant capacity of commercial beverages: application to beers, wines, waters and soft drinks using TRAP, TEAC and FRAP methods.

This work measures and tries to compare the Antioxidant Capacity (AC) of 50 commercial beverages of different kinds: 6 wines, 12 beers, 18 soft drinks and 14 flavoured waters. Because there is no reference procedure established for this purpose, three different optical methods were used to analyse these samples: Total Radical trapping Antioxidant Parameter (TRAP), Trolox Equivalent Antioxidant Capacity (TEAC) and Ferric ion Reducing Antioxidant Parameter (FRAP). These methods differ on the chemical background and nature of redox system. The TRAP method involves the transfer of hydrogen atoms while TEAC and FRAP involves electron transfer reactions. The AC was also assessed against three antioxidants of reference, Ascorbic acid (AA), Gallic acid (GA) and 6-hydroxy-2,5,7,8-tetramethyl- 2-carboxylic acid (Trolox). The results obtained were analyzed statistically. Anova one-way tests were applied to all results and suggested that methods and standards exhibited significant statistical differences. The possible effect of sample features in the AC, such as gas, flavours, food colouring, sweeteners, acidity regulators, preservatives, stabilizers, vitamins, juice percentage, alcohol percentage, antioxidants and the colour was also investigated. The AC levels seemed to change with brand, kind of antioxidants added, and kind of flavour, depending on the sample. In general, higher ACs were obtained for FRAP as method, and beer for kind of sample, and the standard expressing the smaller AC values was GA.

Recommendation of Tourism Resources Supported by Crowdsourcing

Context-aware recommendation of personalised tourism resources is possible because of personal mobile devices and powerful data filtering algorithms. The devices contribute with computing capabilities, on board sensors, ubiquitous Internet access and continuous user monitoring, whereas the filtering algorithms provide the ability to match the profile (interests and the context) of the tourist against a large knowledge bases of tourism resources. While, in terms of technology, personal mobile devices can gather user-related information, including the user context and access multiple data sources, the creation and maintenance of an updated knowledge base of tourism-related resources requires a collaborative approach due to the heterogeneity, volume and dynamic nature of the resources. The current PhD thesis aims to contribute to the solution of this problem by adopting a Crowdsourcing approach for the collaborative maintenance of the knowledge base of resources, Trust and Reputation for the validation of uploaded resources as well as publishers, Big Data for user profiling and context-aware filtering algorithms for the personalised recommendation of tourism resources.

User Profiling and Knowledge Extraction For Tourism

O sector do turismo é uma área francamente em crescimento em Portugal e que tem desenvolvido a sua divulgação e estratégia de marketing. Contudo, apenas se prende com indicadores de desempenho e de oferta instalada (número de quartos, hotéis, voos, estadias), deixando os indicadores estatísticos em segundo plano. De acordo com o “ Travel & tourism Competitiveness Report 2013”, do World Economic Forum, classifica Portugal em 72º lugar no que respeita à qualidade e cobertura da informação estatística, disponível para o sector do Turismo. Refira-se que Espanha ocupa o 3º lugar. Uma estratégia de mercado, sem base analítica, que sustente um quadro de orientações específico e objetivo, com relevante conhecimento dos mercados alvo, dificilmente é compreensível ou até mesmo materializável. A implementação de uma estrutura de Business Intelligence que permita a realização de um levantamento e tratamento de dados que possibilite relacionar e sustentar os resultados obtidos no sector do turismo revela-se fundamental e crucial, para que sejam criadas estratégias de mercado. Essas estratégias são realizadas a partir da informação dos turistas que nos visitam, e dos potenciais turistas, para que possam ser cativados no futuro. A análise das características e dos padrões comportamentais dos turistas permite definir perfis distintos e assim detetar as tendências de mercado, de forma a promover a oferta dos produtos e serviços mais adequados. O conhecimento obtido permite, por um lado criar e disponibilizar os produtos mais atrativos para oferecer aos turistas e por outro informá-los, de uma forma direcionada, da existência desses produtos. Assim, a associação de uma recomendação personalizada que, com base no conhecimento de perfis do turista proceda ao aconselhamento dos melhores produtos, revela-se como uma ferramenta essencial na captação e expansão de mercado.