Social and Semantic Contexts in Tourist Mobile Applications

The ongoing growth of the World Wide Web along with the increase possibility of access information through a variety of devices in mobility, has defi nitely changed the way users acquire, create, and personalize information, pushing innovative strategies for annotating and organizing it. In this scenario, Social Annotation Systems have quickly gained a huge popularity, introducing millions of metadata on di fferent Web resources following a bottom-up approach, generating free and democratic mechanisms of classi cation, namely folksonomies. Moving away from hierarchical classi cation schemas, folksonomies represent also a meaningful mean for identifying similarities among users, resources and tags. At any rate, they suff er from several limitations, such as the lack of specialized tools devoted to manage, modify, customize and visualize them as well as the lack of an explicit semantic, making di fficult for users to bene fit from them eff ectively. Despite appealing promises of Semantic Web technologies, which were intended to explicitly formalize the knowledge within a particular domain in a top-down manner, in order to perform intelligent integration and reasoning on it, they are still far from reach their objectives, due to di fficulties in knowledge acquisition and annotation bottleneck. The main contribution of this dissertation consists in modeling a novel conceptual framework that exploits both social and semantic contextual dimensions, focusing on the domain of tourism and cultural heritage. The primary aim of our assessment is to evaluate the overall user satisfaction and the perceived quality in use thanks to two concrete case studies. Firstly, we concentrate our attention on contextual information and navigation, and on authoring tool; secondly, we provide a semantic mapping of tags of the system folksonomy, contrasted and compared to the expert users' classi cation, allowing a bridge between social and semantic knowledge according to its constantly mutual growth. The performed user evaluations analyses results are promising, reporting a high level of agreement on the perceived quality in use of both the applications and of the speci c analyzed features, demonstrating that a social-semantic contextual model improves the general users' satisfactio

Finding unexplained human behaviors in Social Networks

Detection of human behavior in On-line Social Networks (OSNs) has become a very important challenge for a wide range of appli- cations, such as security, marketing, parent controls and so on, opening a wide range of novel research areas, which have not been fully addressed yet. In this paper, we present a two-stage method for finding unexplained (and potentially anomalous) behaviors in social networks. First, we use Markov chains to automatically learn from the social network graph a number of models of human behaviors (normal behaviors); the second stage applies an activity detection framework based on the concept of possible words to detect all unexplained activities with respect to the well-known behaviors. Some preliminary experiments using Facebook data show the approach efficiency and effectiveness. Copyright © (2014) by Universita Reggio Calabria & Centro di Competenza (ICT-SUD) All rights reserved

Reputation evaluation of georeferenced data for crowd-sensed applications

Volunteered Geographic Information (VGI) is a process where individuals, supported by enabling technologies, behave like physicalsensorstoharvestgeoreferencedcontentintheirsurroundings. Thevalueofthis, typicallyheterogeneous, contenthasbeen recognized by both researchers and organizations. However, in order to be fruitfully used in various VGI-based types of application reliability and quality of particular VGI content (i.e., Points of Interest) have to be assessed. This evaluation can be based on reputation scores that summarize users’ experiences with the specific content. Following this direction, our contribution provides, primarily, a new comprehensive model and a multi-layer architecture for reputation evaluation aimed to assess quality of VGI content. Secondly, we demonstrate the relevance of adopting such a framework through an applicative scenario for recommending touristic itineraries

TOWARDS CLOUD COMPUTING: A SWOT ANALYSIS ON ITS ADOPTION IN SMES

Over the past few years, emergence of Cloud Computing has notably made an evolution in the IT industry by putting forward an ‘everything as a service’ idea .Cloud Computing is of growing interest to companies throughout the world, but there are many barriers associated with its adoption which should be eliminated. This paper aims to investigate Cloud Computing and discusses the drivers and inhibitors of its adoption. Moreover, an attempt has been made to identify the key stakeholders of Cloud Computing and outline the current security challenges. A SWOT analysis which consists of strengths, weaknesses, opportunities and threats has also carried out in which Cloud Computing adoption for SMEs (Small and Medium-sized Enterprises) is evaluated. Finally, the paper concludes with some further research areas in the field of Cloud Computin

Web-Based Recommendation System for Smart Tourism: Multiagent Technology

The purpose of the study is to design and develop a recommended system based on agent and web technologies, which utilizes a hybrid recommendation filtering for the smart tourism industry. A hybrid recommendation system based on agent technology is designed by considering the online communication with other sectors in the tourism industry, such as the tourism supply chain, agency etc. However, online communication between the sectors via agents is designed and developed based on the contract net protocol. Furthermore, the design system is developed on the java agent development framework and implemented as a web application. Case study-based results considering two scenarios involving 100 customers illustrated that the proposed web application improves the rate of the recommendation for the customers. In the first scenario without disturbances, this rate was improved by 20% and the second scenario with disturbances yielded a 30% rate of acceptable recommendation. In addition, based on the second scenario, real time data communication on the system occurred, thus the proposed system supported real time data communication

A Model Driven Approach to Water Resource Analysis based on Formal Methods and Model Transformation

AbstractSeveral frameworks have been proposed in literature in order to cope with critical infrastructure modelling issues, and almost all rely on simulation techniques. Anyway simulation is not enough for critical systems, where any problem may lead to consistent loss in money and even human lives. Formal methods are widely used in order to enact exhaustive analyses of these systems, but their complexity grows with system dimension and heterogeneity. In addition, experts in application domains could not be familiar with formal modelling techniques. A way to manage complexity of analysis is the use of Model Based Transformation techniques: analysts can express their models in the way they use to do and automatic algorithms translate original models into analysable ones, reducing analysis complexity in a completely transparent way.In this work we describe an automatic transformation algorithm generating hybrid automata for the analysis of a natural water supply system. We use real system located in the South of Italy as case study

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality

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

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