2,692 research outputs found

    A Survey on Trust Computation in the Internet of Things

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    Internet of Things defines a large number of diverse entities and services which interconnect with each other and individually or cooperatively operate depending on context, conditions and environments, produce a huge personal and sensitive data. In this scenario, the satisfaction of privacy, security and trust plays a critical role in the success of the Internet of Things. Trust here can be considered as a key property to establish trustworthy and seamless connectivity among entities and to guarantee secure services and applications. The aim of this study is to provide a survey on various trust computation strategies and identify future trends in the field. We discuss trust computation methods under several aspects and provide comparison of the approaches based on trust features, performance, advantages, weaknesses and limitations of each strategy. Finally the research discuss on the gap of the trust literature and raise some research directions in trust computation in the Internet of Things

    Reputation assessment in collaborative environments.

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    The popularity of open collaboration platforms is strongly related to the popularity of Internet: the growing of the latter (in technology and users) is a spring to the former. With the advent of Web 2.0, not only the Internet users became from passive receiver of published content to active producer of content, but also active reviewers and editors of content. With the increase of popularity of these platforms, some new interesting problems arise related on how to choose the best one, how to choose the collaborators and how evaluate the quality of the final work. This evolution has brought much benefit to the Internet community, especially related to the availability of free content, but also gave rise to the problem of how much this content, or these people, may be trusted. The purpose of this thesis is to present different reputation systems suitable for collaborative environments; to show that we must use very different techniques to obtain the best from the data we are dealing with and, eventually, to compare reputations systems and recommender systems and show that, under some strict circumstances, they become similar enough and we can just make minor adjustment to one to obtain the other

    Reputation assessment in collaborative environments.

    Get PDF
    The popularity of open collaboration platforms is strongly related to the popularity of Internet: the growing of the latter (in technology and users) is a spring to the former. With the advent of Web 2.0, not only the Internet users became from passive receiver of published content to active producer of content, but also active reviewers and editors of content. With the increase of popularity of these platforms, some new interesting problems arise related on how to choose the best one, how to choose the collaborators and how evaluate the quality of the final work. This evolution has brought much benefit to the Internet community, especially related to the availability of free content, but also gave rise to the problem of how much this content, or these people, may be trusted. The purpose of this thesis is to present different reputation systems suitable for collaborative environments; to show that we must use very different techniques to obtain the best from the data we are dealing with and, eventually, to compare reputations systems and recommender systems and show that, under some strict circumstances, they become similar enough and we can just make minor adjustment to one to obtain the other

    Manipulating the Capacity of Recommendation Models in Recall-Coverage Optimization

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    Traditional approaches in Recommender Systems ignore the problem of long-tail recommendations. There is no systematic approach to control the magnitude of long-tail recommendations generated by the models, and there is not even proper methodology to evaluate the quality of long-tail recommendations. This thesis addresses the long-tail recommendation problem from both the algorithmic and evaluation perspective. We proposed controlling the magnitude of long-tail recommendations generated by models through the manipulation with capacity hyperparameters of learning algorithms, and we dene such hyperparameters for multiple state-of-the-art algorithms. We also summarize multiple such algorithms under the common framework of the score function, which allows us to apply popularity-based regularization to all of them. We propose searching for Pareto-optimal states in the Recall-Coverage plane as the right way to search for long-tail, high-accuracy models. On the set of exhaustive experiments, we empirically demonstrate the corectness of our theory on a mixture of public and industrial datasets for 5 dierent algorithms and their dierent versions.Traditional approaches in Recommender Systems ignore the problem of long-tail recommendations. There is no systematic approach to control the magnitude of long-tail recommendations generated by the models, and there is not even proper methodology to evaluate the quality of long-tail recommendations. This thesis addresses the long-tail recommendation problem from both the algorithmic and evaluation perspective. We proposed controlling the magnitude of long-tail recommendations generated by models through the manipulation with capacity hyperparameters of learning algorithms, and we dene such hyperparameters for multiple state-of-the-art algorithms. We also summarize multiple such algorithms under the common framework of the score function, which allows us to apply popularity-based regularization to all of them. We propose searching for Pareto-optimal states in the Recall-Coverage plane as the right way to search for long-tail, high-accuracy models. On the set of exhaustive experiments, we empirically demonstrate the corectness of our theory on a mixture of public and industrial datasets for 5 dierent algorithms and their dierent versions

    Reputation assessment in collaborative environments.

    Get PDF
    The popularity of open collaboration platforms is strongly related to the popularity of Internet: the growing of the latter (in technology and users) is a spring to the former. With the advent of Web 2.0, not only the Internet users became from passive receiver of published content to active producer of content, but also active reviewers and editors of content. With the increase of popularity of these platforms, some new interesting problems arise related on how to choose the best one, how to choose the collaborators and how evaluate the quality of the final work. This evolution has brought much benefit to the Internet community, especially related to the availability of free content, but also gave rise to the problem of how much this content, or these people, may be trusted. The purpose of this thesis is to present different reputation systems suitable for collaborative environments; to show that we must use very different techniques to obtain the best from the data we are dealing with and, eventually, to compare reputations systems and recommender systems and show that, under some strict circumstances, they become similar enough and we can just make minor adjustment to one to obtain the other

    Recommender Systems Implementations with Deep Learning

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    Η εξέλιξη του διαδικτύου και η ανεξέλεγκτη αύξηση ροής της πληροφορίας εντός του προσφέρουν πολλά πλεονεκτήματα στους σημερινούς χρήστες, γεννούν όμως ταυτόχρονα ένα απροσδόκητο πρόβλημα: το παράδοξο της επιλογής. Ως απάντηση στο εν λόγω πρόβλημα, έχουν υλοποιηθεί συστήματα προτάσεων σε σχεδόν οτιδήποτε αποτελεί μέρος του διαδικτύου. Αυτοί οι αλγόριθμοι εξόρυξης μεγάλων δεδομένων παρέχουν στους χρήστες όσο το δυνατόν περισσότερες στοχευμένες επιλογές, σε μια προσπάθεια εξατομίκευσης και διευκόλυνσης της εμπειρίας του χρήστη. Ωστόσο, η αποτελεσματικότητα τέτοιων συστημάτων εντείνεται όταν συνδυαστούν με την ακόμη νεαρή τεχνολογία αλγορίθμων βαθιάς μάθησης. Ο σκοπός της παρούσας έρευνας είναι να αναλυθούν τα θετικά στοιχεία κάθε συστήματος ξεχωριστά, ούτως ώστε να αποδειχτεί η αναγκαιότητα του συνδυασμού τους. Επιπλέον, με χρήση προσομοιωμένων παραδειγμάτων, θα δημιουργηθεί και θα δοκιμαστεί μία ενδεικτική υλοποίηση για να υποστηρίξει την θεωρία. Λόγω ελλείψεων όσον αφορά πλήθος πραγματικών χρηστών και υπάρχοντος εξοπλισμού, το έργο αυτό θα επικεντρωθεί στην διαδικαστική προσέγγιση κάθε μεθόδου, προσφέροντας ταυτόχρονα ένα θεωρητικό υπόβαθρο και προσπαθώντας να προβλέψει τα αποτελέσματα της. Στο τελευταίο τμήμα της έρευνας θα θιχτούν ορισμένα προβλήματα βελτιστοποίησης και θα προταθούν ορισμένες πιθανές μη αποδεδειγμένες λύσεις.The rise of the Internet as well as the increasingly uncontrollable flow of information in the web come with multiple advantages for today´s users, yet at the same time giving birth to an unexpected issue: the paradox of choice. To counter this problem, recommender systems have been implemented in almost everything that is part of the Internet. Large-scale data mining algorithms provide users with as many targeted choices as possible, in an effort to personalize and facilitate user experience. However, the effectiveness of such systems truly shines when combined with the still young technology of deep learning algorithms. The purpose of this work is to analyze the advantages of each system separately, in order to prove the necessity of combining them. For that reason, a review of a multitude of deep learning algorithms will be provided. Furthermore, by using a simulated example, one such indicative implementation will be created and tested to support the theory behind it. Due to lack of an actual demographic and adequate equipment, the focus of this paper will be in the procedural approach of each method, while still offering theoretical feedback and trying to predict its outcome. In the final section of this study, a few optimization problems will be addressed, as well as some possible unproven solutions

    Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks

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    Tesis por compendioEn el día a día, las personas suelen confiar en recomendaciones, tradicionalmente aportadas por otras personas (familia, amigos, etc.) para sus decisiones más variadas. En el mundo digital esto no es diferente, dado que los sistemas de recomendación están presentes en todas partes y de modo transparente. El principal objetivo de estos sistemas es el de ayudar en el proceso de toma de decisiones, generando recomendaciones de su interés y basadas en sus gustos. Dichas recomendaciones van desde productos en sitios web de comercio electrónico, como libros o lugares a visitar, además de qué comer o cuánto tiempo uno debe caminar al día para tener una vida sana, con quién salir o a quién seguir en las redes sociales. Esta es un área en ascensión. Por un lado, tenemos cada vez más usuarios en internet cuya vida está digitalizada, dado que lo que se hace en el "mundo real" está representado en cierto modo en el "mundo digital". Por otro lado, sufrimos una sobrecarga de información, que puede mitigarse mediante el uso de un sistema de recomendación. Sin embargo, estos sistemas también enfrentan algunos problemas, como el problema del arranque en frío y su necesidad de ser cada vez más "humanos", "personalizados" y "precisos" para satisfacer las exigencias de usuarios y empresas. En este desafiante escenario, los sistemas de recomendación basados en la personalidad se están estudiando cada vez más, ya que son capaces de enfrentar esos problemas. Algunos proyectos recientes proponen el uso de la personalidad humana en los recomendadores, ya sea en su conjunto o individualmente por rasgos. Esta tesis está dedicada a este nuevo área de recomendación basada en la personalidad, centrándose en uno de sus rasgos más importantes, la curiosidad. Además, para explotar la información ya existente en internet, obtendremos de forma implícita información de las redes sociales. Por lo tanto, este trabajo tiene como objetivo proporcionar una mejor experiencia al usuario final a través de un nuevo enfoque que ofrece una alternativa a algunos de los retos identificados en los sistemas de recomendación basados en la personalidad. Entre estas mejoras, el uso de las redes sociales para alimentar los sistemas de recomendación reduce el problema del arranque en frío y, al mismo tiempo, proporciona datos valiosos para la predicción de la personalidad humana. Por otro lado, la curiosidad no ha sido utilizada por ninguno de los sistemas de recomendación estudiados; casi todos han usado la personalidad general de un individuo a través de los Cinco Grandes rasgos de la personalidad. Sin embargo, los estudios psicológicos confirman que la curiosidad es un rasgo relevante en el proceso de elegir un item, cuestión directamente relacionada con los sistemas de recomendación. En resumen, creemos que un sistema de recomendación que mida implícitamente la curiosidad y la utilice en el proceso de recomendar nuevos ítems, especialmente en el sector turístico, podría claramente mejorar la capacidad de estos sistemas en términos de precisión, serendipidad y novedad, permitiendo a los usuarios obtener niveles positivos de satisfacción con las recomendaciones. Esta tesis realiza un estudio exhaustivo del estado del arte, donde destacamos trabajos sobre sistemas de recomendación, la personalidad humana desde el punto de vista de la psicología tradicional y positiva y finalmente cómo se combinan ambos aspectos. Luego, desarrollamos una aplicación en línea capaz de extraer implícitamente información del perfil de usuario en una red social, generando predicciones de uno o más rasgos de su personalidad. Finalmente, desarrollamos el sistema CURUMIM, capaz de generar recomendaciones en línea con diferentes propiedades, combinando la curiosidad y algunas características sociodemográficas (como el nivel de educación) extraídas de Facebook. El sistema ha sido probado y evaluado en el contexto turístico por usuarios rEn el dia a dia, les persones solen confiar en recomanacions, tradicionalment aportades per altres persones (família, amics, etc.) per a les seues decisions més variades. En el món digital això no és diferent, atès que els sistemes de recomanació estan presents a tot arreu i de manera transparent. El principal objectiu d'aquests sistemes és el d'ajudar en el procés de presa de decisions, generant recomanacions del seu interès i basades en els seus gustos. Aquestes recomanacions van des de productes en pàgines web de comerç electrònic, com a llibres o llocs a visitar, a més de què menjar o quant temps una persona ha de caminar al dia per a tindre una vida sana, amb qui eixir o a qui seguir en les xarxes socials. Aquesta és una àrea en ascensió. D'una banda, tenim cada vegada més usuaris en internet la vida de les quals està digitalitzada, atès que el que es fa en el "món real" està representat en certa manera en el "món digital". D'altra banda, patim una sobrecàrrega d'informació, que pot mitigar-se mitjançant l'ús d'un sistema de recomanació. No obstant això, aquests sistemes també enfronten alguns problemes, com el problema de l'arrencada en fred i la seua necessitat de ser cada vegada més "humans", "personalitzats" i "precisos" per a satisfer les exigències d'usuaris i empreses. En aquest desafiador escenari, els sistemes de recomanació basats en la personalitat s'estan estudiant cada vegada més, ja que són capaços d'enfrontar eixos problemes. Alguns projectes recents proposen l'ús de la personalitat humana en els recomendadors, ja siga en el seu conjunt o individualment per trets. Aquesta tesi està dedicada a aquest nou àrea de recomanació basada en la personalitat, centrant-se en un dels seus trets més importants, la curiositat. A més, per a explotar la informació ja existent en internet, obtindrem de forma implícita informació de les xarxes socials. Per tant, aquest treball té com a objectiu proporcionar una millor experiència a l'usuari final a través d'un nou enfocament que ofereix una alternativa a alguns dels reptes identificats en els sistemes de recomanació basats en la personalitat. Entre aquestes millores, l'ús de les xarxes socials per a alimentar els sistemes de recomanació redueix el problema de l'arrencada en fred i, al mateix temps, proporciona dades valuoses per a la predicció de la personalitat humana. D'altra banda, la curiositat no ha sigut utilitzada per cap dels sistemes de recomanació estudiats; quasi tots han usat la personalitat general d'un individu a través dels Cinc Grans trets de la personalitat. No obstant això, els estudis psicològics confirmen que la curiositat és un tret rellevant en el procés de triar un item, qüestió directament relacionada amb els sistemes de recomanació. En resum, creiem que un sistema de recomanació que mesure implícitament la curiositat i la utilitze en el procés de recomanar nous ítems, especialment en el sector turístic, podria clarament millorar la capacitat d'aquests sistemes en termes de precisió, sorpresa i novetat, permetent als usuaris obtindre nivells positius de satisfacció amb les recomanacions. Aquesta tesi realitza un estudi exhaustiu de l'estat de l'art, on destaquem treballs sobre sistemes de recomanació, la personalitat humana des del punt de vista de la psicologia tradicional i positiva i finalment com es combinen tots dos aspectes. Després, desenvolupem una aplicació en línia capaç d'extraure implícitament informació del perfil d'usuari en una xarxa social, generant prediccions d'un o més trets de la seua personalitat. Finalment, desenvolupem el sistema CURUMIM, capaç de generar recomanacions en línia amb diferents propietats, combinant la curiositat i algunes característiques sociodemogràfiques (com el nivell d'educació) extretes de Facebook. El sistema ha sigut provat i avaluat en el context turístic per usuaris reals. Els resultats demostren la seua capacitat perIn daily life, people usually rely on recommendations, traditionally given by other people (family, friends, etc.) for their most varied decisions. In the digital world, this is not different, given that recommender systems are present everywhere in such a way that we no longer realize. The main goal of these systems is to assist users in the decision-making process, generating recommendations that are of their interest and based on their tastes. These recommendations range from products in e-commerce websites, like books to read or places to visit to what to eat or how long one should walk a day to have a healthy life, who to date or who one should follow on social networks. And this is an increasing area. On the one hand, we have more and more users on the internet whose life is somewhat digitized, given than what one does in the "real world" is represented in a certain way in the "digital world". On the other hand, we suffer from information overload, which can be mitigated by the use of recommendation systems. However, these systems also face some problems, such as the cold start problem and their need to be more and more "human", "personalised" and "precise" in order to meet the yearning of users and companies. In this challenging scenario, personality-based recommender systems are being increasingly studied, since they are able to face these problems. Some recent projects have proposed the use of the human personality in recommenders, whether as a whole or individually by facet in order to meet those demands. Therefore, this thesis is devoted to this new area of personality-based recommendation, focusing on one of its most important traits, the curiosity. Additionally, in order to exploit the information already present on the internet, we will implicitly obtain information from social networks. Thus, this work aims to build a better experience for the end user through a new approach that offers an option for some of the gaps identified in personality-based recommendation systems. Among these gap improvements, the use of social networks to feed the recommender systems soften the cold start problem and, at the same time, it provides valuable data for the prediction of the human personality. Another found gap is that the curiosity was not used by any of the studied recommender systems; almost all of them have used the overall personality of an individual through the Big Five personality traits. However, psychological studies confirm that the curiosity is a relevant trait in the process of choosing an item, which is directly related to recommendation systems. In summary, we believe that a recommendation system that implicitly measures the curiosity and uses it in the process of recommending new items, especially in the tourism sector, could clearly improve the capacity of these systems in terms of accuracy, serendipity and novelty, allowing users to obtain positive levels of satisfaction with the recommendations. This thesis begins with an exhaustive study of the state of the art, where we highlight works about recommender systems, the human personality from the point of view of traditional and positive psychology and how these aspects are combined. Then, we develop an online application capable of implicitly extracting information from the user profile in a social network, thus generating predictions of one or more personality traits. Finally, we develop the CURUMIM system, able to generate online recommendations with different properties, combining the curiosity and some sociodemographic characteristics (such as level of education) extracted from Facebook. The system is tested and assessed within the tourism context by real users. The results demonstrate its ability to generate novel and serendipitous recommendations, while maintaining a good level of accuracy, independently of the degree of curiosity of the users.Menk Dos Santos, A. (2018). Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/114798TESISCompendi
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