An analysis on the impact of geolocation in recommending venues in location-based social networks

The pervasiveness of geo-located devices has opened new possibilities in recommender systems on social networks. In effect, Location-Based Social Networks or LBSNs are a relatively new breed of social networks that let users share their location by triggering ”check-in” events on venues, such as businesses or historical places. In this paper, we compare the performance of traditional rating and social-based similarity metrics against location-based metrics in a userbased collaborative filtering algorithm that recommends venues or places to visit. This analysis was performed on a large real-world dataset provided by the Yelp social network service. Our results show that, geo-located metrics perform as well as rating or social metrics for selecting like-minded users and, thus, to issue a recommendation.Sociedad Argentina de Informática e Investigación Operativ

On the Role of Personality Traits in Followee Recommendation Algorithms

Followee recommendation is a problem rapidly gaining importance in Twitter and other micro-blogging communities. Most traditional recommendation systems only rely on content or topology, disregarding the effect of psychological characteristics over the followee selection process. As personality is considered one of the primary factors that influence human behaviour, this study aims at assessing the impact of personality in the accurate prediction of followees. It analyses whether user personality could condition followee selection by combining personality traits with the most common followee recommendation factors.Sociedad Argentina de Informática e Investigación Operativa (SADIO

On the Role of Personality Traits in Followee Recommendation Algorithms

Followee recommendation is a problem rapidly gaining importance in Twitter and other micro-blogging communities. Most traditional recommendation systems only rely on content or topology, disregarding the effect of psychological characteristics over the followee selection process. As personality is considered one of the primary factors that influence human behaviour, this study aims at assessing the impact of personality in the accurate prediction of followees. It analyses whether user personality could condition followee selection by combining personality traits with the most common followee recommendation factors.Sociedad Argentina de Informática e Investigación Operativa (SADIO

An analysis on the impact of geolocation in recommending venues in location-based social networks

The pervasiveness of geo-located devices has opened new possibilities in recommender systems on social networks. In effect, Location-Based Social Networks or LBSNs are a relatively new breed of social networks that let users share their location by triggering ”check-in” events on venues, such as businesses or historical places. In this paper, we compare the performance of traditional rating and social-based similarity metrics against location-based metrics in a userbased collaborative filtering algorithm that recommends venues or places to visit. This analysis was performed on a large real-world dataset provided by the Yelp social network service. Our results show that, geo-located metrics perform as well as rating or social metrics for selecting like-minded users and, thus, to issue a recommendation.Sociedad Argentina de Informática e Investigación Operativ

An analysis on the impact of geolocation in recommending venues in location-based social networks

The pervasiveness of geo-located devices has opened new possibilities in recommender systems on social networks. In effect, Location-Based Social Networks or LBSNs are a relatively new breed of social networks that let users share their location by triggering ”check-in” events on venues, such as businesses or historical places. In this paper, we compare the performance of traditional rating and social-based similarity metrics against location-based metrics in a userbased collaborative filtering algorithm that recommends venues or places to visit. This analysis was performed on a large real-world dataset provided by the Yelp social network service. Our results show that, geo-located metrics perform as well as rating or social metrics for selecting like-minded users and, thus, to issue a recommendation.Sociedad Argentina de Informática e Investigación Operativ

Paynal: Una herramienta de Ingeniería de Software Colaborativa

En este trabajo se describe una aplicación, llamada Paynal, que provee herramientas groupware al entorno de desarrollo Eclipse, permitiendo a sus usuarios interactuar mediante chats, foros y mensajes. Dichas interacciones generan un vínculo social y, en conjunto, las mismas conforman una Red Social. Mediante el uso de métricas de Análisis de Redes Sociales se puede extraer información para la detección de líderes de grupo, la identi cación de reemplazantes para desarrolladores que abandonan la empresa, entre otros. Utilizando técnicas de Minería de Texto se pueden extraer tópicos de las interacciones entre dos usuarios, permitiendo discernir temas y habilidades. Dichos tópicos son visualizados utilizando Nubes de Tags: una representación compacta y, a su vez, altamente expresiva. Con el objetivo de realizar experiencias reales, se experimentó con datos provenientes de una organización de desarrollo de software, y luego, utilizando dichos datos, se elaboraron conclusiones utilizando las herramientas provistas por Paynal.Sociedad Argentina de Informática e Investigación Operativ

DPM: A novel distributed large-scale social graph processing framework for link prediction algorithms

Large-scale graphs have become ubiquitous in social media. Computer-based recommendations in these huge graphs pose challenges in terms of algorithm design and resource usage efficiency when processing recommendations in distributed computing environments. Moreover, recommendation algorithms for graphs, particularly link prediction algorithms, have different requirements depending of the way the underlying graph is traversed. Path-based algorithms usually perform traversals in different directions to build a large ranking of vertices to recommend, whereas random walk-based algorithms build an initial subgraph and perform several iterations on those vertices to compute the final ranking. In this work, we propose a distributed graph processing framework called Distributed Partitioned Merge (DPM), which supports both types of algorithms and we compare its performance and resource usage w.r.t. two relevant frameworks, namely Fork-Join and Pregel. In our experiments, we show that in most tests DPM outperforms both Pregel and Fork-Join in terms of recommendation time, with a minor penalization in network usage in some scenarios.Fil: Corbellini, Alejandro. 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: Mateos Diaz, Cristian Maximiliano. 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: Schiaffino, Silvia Noemi. 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: Zunino Suarez, Alejandro Octavio. 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

On the Role of Personality Traits in Followee Recommendation Algorithms

Followee recommendation is a problem rapidly gaining importance in Twitter and other micro-blogging communities. Most traditional recommendation systems only rely on content or topology, disregarding the effect of psychological characteristics over the followee selection process. As personality is considered one of the primary factors that influence human behaviour, this study aims at assessing the impact of personality in the accurate prediction of followees. It analyses whether user personality could condition followee selection by combining personality traits with the most common followee recommendation factors.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Nosocomial outbreak of the pandemic Influenza A (H1N1) 2009 in critical hematologic patients during seasonal influenza 2010-2011: detection of oseltamivir resistant variant viruses

BACKGROUND: The pandemic influenza A (H1N1) 2009 (H1N1pdm09) virus infection caused illness and death among people worldwide, particularly in hematologic/oncologic patients because influenza infected individuals can shed virus for prolonged periods, thus increasing the chances for the development of drug-resistant strains such as oseltamivir-resistant (OST-r) variant. METHODS: The aim of our study was to retrospectively evaluate the clinical importance of OST-r variant in circulating strains of the pandemic H1N1pdm09 virus. By means of RT-PCR and Sanger sequencing we analysed the presence of OST-r variant in 76 H1N1pdm09 laboratory-confirmed cases, hospitalized at the hematologic/oncologic ward at Spedali Civili of Brescia –Italy. RESULTS: Out of 76 hospitalized hematologic/oncologic patients, 23 patients (30.2%) were infected by H1N1pdm09 virus. Further investigation revealed that 3 patients were positive for the OST-r variant carrying the H275Y mutation. All the 23 infected patients were immuno-compromised, and were under treatment or had been treated previously with oseltamivir. Three patients died (13%) after admission to intensive care unit and only one of them developed H275Y mutation. CONCLUSIONS: Our retrospective observational study shows that pandemic influenza A (H1N1) 2009 virus can cause significant morbidity and even mortality in hematologic/oncologic patients and confirms the high rate of nosocomial transmission of pandemic H1N1pdm09 virus in these critical subjects. Indeed, the reduction in host defences in these hospitalized patients favoured the prolonged use of antiviral therapy and permitted the development of OST-r strain. Strategies as diagnostic vigilance, early isolation of patients and seasonal influenza A(H1N1) vaccination may prevent transmission of influenza in high risk individuals