1,721,138 research outputs found
Los ríos perdidos de Londres: El sublime topográfico (2016) de Iain Sinclair
No full textFil: Mazzitelli Mastricchio, Malena. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación. Instituto de Investigaciones en Humanidades y Ciencias Sociales (UNLP-CONICET); Argentina.Fil: Godoy, Daniela María. Universidad Autónoma de Entre Ríos. Facultad de Humanidades Artes y Ciencias Sociales
Los ríos perdidos de Londres: El sublime topográfico (2016) de Iain Sinclair
No full textFil: Mazzitelli Mastricchio, Malena. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación. Instituto de Investigaciones en Humanidades y Ciencias Sociales (UNLP-CONICET); Argentina.Fil: Godoy, Daniela María. Universidad Autónoma de Entre Ríos. Facultad de Humanidades Artes y Ciencias Sociales
Los ríos perdidos de Londres: El sublime topográfico (2016) de Iain Sinclair
No full textFil: Mazzitelli Mastricchio, Malena. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación. Instituto de Investigaciones en Humanidades y Ciencias Sociales (UNLP-CONICET); Argentina.Fil: Godoy, Daniela María. Universidad Autónoma de Entre Ríos. Facultad de Humanidades Artes y Ciencias Sociales
User Profiling for Web page filtering
Full text linkTo help address pressing problems with information overload, researchers have developed personal agents to provide assistance to users in navigating the Web. To provide suggestions, such agents rely on user profiles representing interests and preferences, which makes acquiring and modeling interest categories a critical component in their design. Existing profiling approaches have only partially tackled the characteristics that distinguish user profiling from related tasks. The authors' technique generates readable user profiles that accurately capture interests, starting from observations of user behavior on the Web.Fil: 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
Leveraging semantic similarity for folksonomy-based recommendation
Full text linkFor recommending interesting resources, such as Web pages or pictures available in social tagging systems, assessing their similarity with user profiles is crucial. Here, we analyze the role of semantic similarity to calculate the resemblance between user profiles and published resources in folksonomies. Experiments carried out with data from two social sites showed that associating semantics to tags results in more accurate similarities among elements in tagging systems and, consequently, enhances recommendations.Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Rodriguez, Gustavo. Universidad Nacional del Centro de la Provincia de Buenos Aires; ArgentinaFil: Scavuzzo, Franco. Universidad Nacional del Centro de la Provincia de Buenos Aires; Argentin
A User Profiling Architecture for Textual-Based Agents
Full text linkSeveral intelligent agents have been developed in the last decade to help users with the vast amount of information available in the World Wide Web (WWW). Despite the efficiency of these agents depend on the knowledge they have about users, which is contained into user profiles, there are diverse considerations about what a profile should contain and how to construct it. Due to this fact, developers have to face the problem of, not only specifying the user profile content each time, but also its acquisition and adaptation to change in user interests. In this work we present an architecture which prescribes these aspects of the user profiling task. The goal of this architecture is to guide developers in the construction of agents involved with textual-based tasks. The results obtained from its application in a search agent, called PersonalSearcher, are also reported in this work.Fil: 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
Short-text learning in social media: A review
No full textSocial networks occupy an ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with the continuous grow of social media data, creates an imperative need for content organisation. Thus, large-scale text learning tasks in social environments arise as one of the most relevant problems in machine learning and data mining. Interestingly, social media data poses several challenges due to its sparse, high-dimensional and large-volume characteristics. This survey reviews the field of social media data learning, focusing on classification and clustering techniques, as they are two of the most frequent learning tasks. It reviews not only new techniques that have been developed to tackle the new challenges posed by short-texts, but also how traditional techniques can be adapted to overcome such challenges. Then, open issues and research opportunities for social media data learning are discussed.Fil: Tommasel, Antonela. 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; Argentin
Modeling interests of Web users for recommendation: A user profiling approach and trends
No full textIn order to personalize Web-based tasks, personal agents rely on representations of user interests and preferences contained in user profiles. In consequence, a critical component for these agents is their capacity to acquire and model user interest categories as well as adapt them to changes in user interests over time. In this chapter, we address the problem of modeling the information preferences of Web users and its distinctive characteristics. We discuss the limitations of current profiling approaches and present a novel user profiling technique, named WebProfiler, developed to support incremental learning and adaptation of user profiles in agents assisting users with Web-based tasks. This technique aims at acquiring comprehensible user profiles that accurately capture user interests starting from observation of user behavior on the Web.Fil: 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
Link Recommendation in E-Learning Systems Based on Content-Based Student Profiles
No full textE-learning systems present to students learning material carefully prepared and organized by teachers to meet some course goals. However, further relevant information that can help students to complete their learning process about different subjects can also be found on the Web in the form of Web pages, articles, encyclopedias, dictionaries, etc. In this chapter, we present a personalized recommendation approach to suggest relevant Web pages to students according to the context of the activities they are carrying out and their content-based profiles. Learning of user profiles is based on a clustering analysis of learning experiences captured through observation. Afterwards, the learned profiles as well as the more recently accessed documents in a Web-based learning system are used to recommend pages gathered by searching the Web.Fil: 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
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