Followee recommendation based on text analysis of micro-blogging activity

Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity offered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower receives all the micro-blogs from his/her followees. Recent research efforts on understanding micro-blogging as a novel form of communication and news spreading medium, have identified three different categories of users in these systems: information sources, information seekers and friends. As social networks grow in the number of registered users, finding relevant and reliable users to receive interesting information becomes essential. In this paper we propose a followee recommender system based on both the analysis of the content of micro-blogs to detect users´ interests and in the exploration of the topology of the network to find candidate users for recommendation. Experimental evaluation was conducted in order to determine the impact of different profiling strategies based on the text analysis of micro-blogs as well as several factors that allows the identification of users acting as good information sources. We found that user-generated content available in the network is a rich source of information for profiling users and finding like-minded people.Fil: Armentano, Marcelo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Uma ferramenta unificada para projeto, desenvolvimento, execução e recomendação de experimentos de aprendizado de máquina

Orientadores: Ricardo da Silva Torres, Anderson de Rezende RochaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Devido ao grande crescimento do uso de tecnologias para a aquisição de dados, temos que lidar com grandes e complexos conjuntos de dados a fim de extrair conhecimento que possa auxiliar o processo de tomada de decisão em diversos domínios de aplicação. Uma solução típica para abordar esta questão se baseia na utilização de métodos de aprendizado de máquina, que são métodos computacionais que extraem conhecimento útil a partir de experiências para melhorar o desempenho de aplicações-alvo. Existem diversas bibliotecas e arcabouços na literatura que oferecem apoio à execução de experimentos de aprendizado de máquina, no entanto, alguns não são flexíveis o suficiente para poderem ser estendidos com novos métodos, além de não oferecerem mecanismos que permitam o reuso de soluções de sucesso concebidos em experimentos anteriores na ferramenta. Neste trabalho, propomos um arcabouço para automatizar experimentos de aprendizado de máquina, oferecendo um ambiente padronizado baseado em workflow, tornando mais fácil a tarefa de avaliar diferentes descritores de características, classificadores e abordagens de fusão em uma ampla gama de tarefas. Também propomos o uso de medidas de similaridade e métodos de learning-to-rank em um cenário de recomendação, para que usuários possam ter acesso a soluções alternativas envolvendo experimentos de aprendizado de máquina. Nós realizamos experimentos com quatro medidas de similaridade (Jaccard, Sorensen, Jaro-Winkler e baseada em TF-IDF) e um método de learning-to-rank (LRAR) na tarefa de recomendar workflows modelados como uma sequência de atividades. Os resultados dos experimentos mostram que a medida Jaro-Winkler obteve o melhor desempenho, com resultados comparáveis aos observados para o método LRAR. Em ambos os casos, as recomendações realizadas são promissoras, e podem ajudar usuários reais em diferentes tarefas de aprendizado de máquinaAbstract: Due to the large growth of the use of technologies for data acquisition, we have to handle large and complex data sets in order to extract knowledge that can support the decision-making process in several domains. A typical solution for addressing this issue relies on the use of machine learning methods, which are computational methods that extract useful knowledge from experience to improve performance of target applications. There are several libraries and frameworks in the literature that support the execution of machine learning experiments. However, some of them are not flexible enough for being extended with novel methods and they do not support reusing of successful solutions devised in previous experiments made in the framework. In this work, we propose a framework for automating machine learning experiments that provides a workflow-based standardized environment and makes it easy to evaluate different feature descriptors, classifiers, and fusion approaches in a wide range of tasks. We also propose the use of similarity measures and learning-to-rank methods in a recommendation scenario, in which users may have access to alternative machine learning experiments. We performed experiments with four similarity measures (Jaccard, Sorensen, Jaro-Winkler, and a TF-IDF-based measure) and one learning-to-rank method (LRAR) in the task of recommending workflows modeled as a sequence of activities. Experimental results show that Jaro-Winkler yields the highest effectiveness performance with comparable results to those observed for LRAR. In both cases, the recommendations performed are very promising and might help real-world users in different daily machine learning tasksMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Reverse k-Ranks Queries on Large Graphs

published_or_final_versio

Privacy-preserving friend recommendations in online social networks

Online social networks, such as Facebook and Google+, have been emerging as a new communication service for users to stay in touch and share information with family members and friends over the Internet. Since the users are generating huge amounts of data on social network sites, an interesting question is how to mine this enormous amount of data to retrieve useful information. Along this direction, social network analysis has emerged as an important tool for many business intelligence applications such as identifying potential customers and promoting items based on their interests. In particular, since users are often interested to make new friends, a friend recommendation application provides the medium for users to expand his/her social connections and share information of interest with more friends. Besides this, it also helps to enhance the development of the entire network structure. The existing friend recommendation methods utilize social network structure and/or user profile information. However, these methods can no longer be applicable if the privacy of users is taken into consideration. This work introduces a set of privacy-preserving friend recommendation protocols based on different existing similarity metrics in the literature. Briefly, depending on the underlying similarity metric used, the proposed protocols guarantee the privacy of a user\u27s personal information such as friend lists. These protocols are the first to make the friend recommendation process possible in privacy-enhanced social networking environments. Also, this work considers the case of outsourced social networks, where users\u27 profile data are encrypted and outsourced to third-party cloud providers who provide social networking services to the users. Under such an environment, this work proposes novel protocols for the cloud to do friend recommendations in a privacy-preserving manner --Abstract, page iii

Estabelecimento de redes de comunidades sobreponíveis

Doutoramento em Engenharia InformáticaUma das áreas de investigação em Telecomunicações de interesse crescente prende-se com os futuros sistemas de comunicações móveis de 4a geração e além destes. Nos últimos anos tem sido desenvolvido o conceito de redes comunitárias, no qual os utilizadores se agregam de acordo com interesses comuns. Estes conceitos têm sido explorados de uma forma horizontal em diferentes camadas da comunicação, desde as redes comunitárias de comunicação (Seattle Wireless ou Personal Telco, p.ex.) até às redes de interesses peer-to-peer. No entanto, estas redes são usualmente vistas como redes de overlay, ou simplesmente redes de associação livre. Na prática, a noção de uma rede auto-organizada, completamente orientada ao serviço/comunidade, integralmente suportada em termos de arquitetura, não existe. Assim este trabalho apresenta uma realização original nesta área de criação de redes comunitárias, com uma arquitetura subjacente orientada a serviço, e que suporta integralmente múltiplas redes comunitárias no mesmo dispositivo, com todas as características de segurança, confiança e disponibilização de serviço necessárias neste tipo de cenários (um nó pode pertencer simultaneamente a mais do que uma rede comunitária). Devido à sua importância para os sistemas de redes comunitárias, foi dado particular atenção a aspetos de gestão de recursos e controlo de acessos. Ambos realizados de uma forma descentralizada e considerando mecanismos dotados de grande escalabilidade. Para isso, é apresentada uma linguagem de políticas que suporta a criação de comunidades virtuais. Esta linguagem não é apenas utilizada para o mapeamento da estrutura social dos membros da comunidade, como para, gerir dispositivos, recursos e serviços detidos pelos membros, de uma forma controlada e distribuída.One of the research areas with increasing interest in the field of telecommunications, are the ones related to future telecommunication systems, both 4th generation and beyond. In parallel, during the last years, several concepts have been developed related to clustering of users according to their interested, in the form of community networks. Solutions proposed for these concepts tackle the challenges horizontally, for each layer of the communication stack, ranging from community based communication networks (e.g. Seattle Wireless, or Personal Telco), to interest networks based on peer-to-peer protocols. However, these networks are presented either as free joining, or overlay networks. In practice, the notion of a self-organized, service and community oriented network, with these principles embedded in its design principles, is yet to be developed. This work presents an novel instantiation of a solution in the area of community networks, with a underlying architecture which is fully service oriented, and envisions the support for multiple community networks in the same device. Considerations regarding security, trust and service availability for this type of environments are also taken. Due to the importance of resource management and access control, in the context of community driven communication networks, a special focus was given to the support of scalable and decentralized management and access control methods. For this purpose, it is presented a policy language which supports the creation and management of virtual communities. The language is not only used for mapping the social structure of the community members, but also to, following a distributed approach, manage devices, resources and services owned by each community member

Técnicas de agrupamento de trajetórias com aplicação à recomendação de percursos

Mestrado em Engenharia de Computadores e TelemáticaO uso generalizado de dispositivos capazes de obter e transmitir dados sobre a localização de objetos ao longo do tempo tem permitido recolher grandes volumes de dados espácio-temporais. Por isso, tem-se assistido a uma procura crescente de técnicas e ferramentas para a análise de grandes volumes de dados espácio-temporais com o intuito de disponibilizar uma gama variada de serviços baseados na localização. Esta dissertação centra-se no desenvolvimento de um sistema para recomendaSr trajetos com base em dados históricos sobre a localização de objetos móveis ao longo do tempo. O principal problema estudado neste trabalho consiste no agrupamento de trajetórias e na extração de informação a partir dos grupos de trajetórias. Este estudo, não se restringe a dados provenientes apenas de veículos, podendo ser aplicado a outros tipos de trajetórias, por exemplo, percursos realizados por pessoas a pé ou de bicicleta. O agrupamento baseia-se numa medida de similaridade. A extração de informação consiste em criar uma trajetória representativa para cada grupo de trajetórias. As trajetórias representativas podem ser visualizadas usando uma aplicação web, sendo também possível configurar cada módulo do sistema com parâmetros desejáveis, na sua maioria distâncias limiares. Por fim, são apresentados casos de teste para avaliar o desempenho global do sistema desenvolvido.The widespread use of devices to capture and transmit data about the location of objects over time allows collecting large volumes of spatio-temporal data. Consequently, there has been in recent years a growing demand for tools and techniques to analyze large volumes of spatio-temporal data aiming at providing a wide range of location-based services. This dissertation focuses on the development of a system for recommendation of trajectories based on historical data about the location of moving objects over time. The main issues covered in this work are trajectory clustering and extracting information from trajectory clusters. This study is not restricted to data from vehicles and can also be applied to other kinds of trajectories, for example, the movement of runners or bikes. The clustering is based on a similarity measure. The information extraction consists in creating a representative trajectory for the trajectories clusters. Finally, representative trajectories are displayed using a web application and it is also possible to configure each system module with desired parameters, mostly distance thresholds. Finally, case studies are presented to evaluate the developed system

Adaptive notifications to support knowledge sharing in virtual communities

Social web-groups where people with common interests and goals communicate, share resources, and construct knowledge, are becoming a major part of today’s organisational practice. Research has shown that appropriate support for effective knowledge sharing tailored to the needs of the community is paramount. This brings a new challenge to user modelling and adaptation, which requires new techniques for gaining sufficient understanding of a virtual community (VC) and identifying areas where the community may need support. The research presented here addresses this challenge presenting a novel computational approach for community-tailored support underpinned by organisational psychology and aimed at facilitating the functioning of the community as a whole (i.e. as an entity). A framework describing how key community processes—transactive memory (TM), shared mental models (SMMs), and cognitive centrality (CCen)—can be utilised to derive knowledge sharing patterns from community log data is described. The framework includes two parts: (i) extraction of a community model that represents the community based on the key processes identified and (ii) identification of knowledge sharing behaviour patterns that are used to generate adaptive notifications. Although the notifications target individual members, they aim to influence individuals’ behaviour in a way that can benefit the functioning of the community as a whole. A validation study has been performed to examine the effect of community-adapted notifications on individual members and on the community as a whole using a close-knit community of researchers sharing references. The study shows that notification messages can improve members’ awareness and perception of how they relate to other members in the community. Interesting observations have been made about the linking between the physical and the VC, and how this may influence members’ awareness and knowledge sharing behaviour. Broader implications for using log data to derive community models based on key community processes and generating community-adapted notifications are discussed

Graph databases and their application to the Italian Business Register for efficient search of relationships among companies

We studied and tested three of the major graph databases, and we compared them with a relational database. We worked on a dataset representing equity participations among companies, and we found out that the strong points of graph databases are: the purposely designed storage techniques; and their query languages. The main performance increments have been obtained when heavy graph situations are queried; for simpler situations and queries, a relational database performs equally wellope

The Use Of Non-Invasive Fibrosis Markers In Stratification Care Pathways For The Management Of Chronic Liver Disease

The health, societal and economic consequences of chronic liver disease (CLD) are substantial and increasing exponentially. Cirrhosis is typically detected in the latter stages when prognosis is poor. Timely diagnosis is hindered by reliance on non-discriminatory tests for fibrosis. I explored the role of non-invasive tests (NITs) of liver fibrosis in primary care to promote earlier disease detection. In this thesis, a systematic review revealed a paucity of published studies evaluating NIT in the community setting. A national survey demonstrated that UK specialists consider current fibrosis assessment methods to be sub-optimal, and NIT are important in improving disease stratification in primary care. To benchmark standard care, a one-year retrospective study of GP referrals for non-alcoholic fatty liver disease (NAFLD) established 93% of referrals to have non-significant fibrosis (Brunt ≤ F2) as assessed by liver specialists. Over two-thirds had a low-risk FIB-4 (<1.30) and could have avoided referral, although a quarter of patients with indeterminate FIB-4 (1.30 – 3.25) had significant liver fibrosis suggesting patients in this subgroup warrant further evaluation. As part of the Camden and Islington liver working group, I developed and evaluated a NAFLD pathway that employs FIB-4 and ELF to identify patients with advanced fibrosis or cirrhosis (Brunt ≥ F3 fibrosis). The pathway processed nearly 1500 patients over two years, resulting in a reduction in the proportion of total patients referred and an 81% decrease in referral of patients with non-significant fibrosis. The pathway achieved a 5-fold increase in the referral of patients with advanced fibrosis and 3-fold increase in the detection of liver cirrhosis. To further extrapolate these findings, I developed a probabilistic decision analytical model which tested FIB-4, ELF and fibroscan, either alone or in combination in primary care pathways. Cost consequence analyses revealed all strategies to be clinically effective and cost-saving compared to standard care