1,721,019 research outputs found
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
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
One-class support vector machines for personalized tag-based resource classification in social bookmarking systems
Full text linkSocial tagging systems allow users to easily create, organize and share collections of Web resources in a collaborative fashion. Videos, pictures, research papers and Web pages are shared and annotated in sites such as Del.icio.us, CiteULike or Flickr, among others. The rising popularity of these systems leads to a constant increase in the number of users actively publishing and annotating resources and, consequently, an exponential growth in the amount of data contained in their folksonomies, the underlying data structure of tagging systems. In turn, the user task of discovering interesting resources becomes more and more difficult and time-consuming. In this paper the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class Support Vector Machine (SVM) classification is evaluated as a means to identify relevant information for users based exclusively on positive examples of their information preferences. It is assumed that users express their interest on resources belonging to a folksonomy by assigning tags to them, whereas there is not an straightforward method to collect uninterestingness judgments. Filtering interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities of Web communities. In this paper, the results achieved with tag-based classification are compared with those obtained using more traditional information sources such as the full-text of Web pages. Experimental evaluation showed that tag-based classifiers outperformed those learned using the text of documents as well as other content-related sources. Moreover, tag-based classification becomes essential for folksonomies in which no additional content is available because of the nature of resources being stored (e.g. tagging of photos or videos).Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin
Semantic grounding of social annotations for enhancing resource classification in folksonomies
Full text linkUser-generated annotations in tagging or bookmarking sites such as Flickr or Delicious can provide a promising and interesting source of information for aiding tasks such as Web resource classification. However, the use of tags brings up some challenges. Since there are no constraints on the terms that can be used for tagging, noise and ambiguity are introduced when users annotate resources. Moreover, traditional bag-of-words representations ignore connections between terms and, thus, are affected by synonymity and hyponymia. Althougth tag-based representations are a valuable source for classifying resources, the problems associated with the unsupervised nature of tags may hinder classification results. This paper presents an approach for semantically analysing social annotations in order to attain enriched concept-based representations of Web resources. Representations are enriched with concepts extracted from WordNet and Wikipedia to overcome problems caused by natural language as well as enhancing the quality of information available for performing an effective classification of resources. Several strategies for tag pre-processing, concept disambiguation and incorporation of semantic entities to representations are discussed and evaluated in this paper. Experimental results showed that the strategies proposed to associate tags with conceptual entities allow improving resource classification results, outperforming traditional approaches based on bag-of-words representations.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
Mining interests for user profiling in electronic conversations
Full text linkThe increasing amount of Web-based tasks is currently requiring personalization strategies to improve the user experience. However, building user profiles is a hard task, since users do not usually give explicit information about their interests. Therefore, interests must be mined implicitly from electronic sources, such as chat and discussion forums. In this work, we present a novel method for topic detection from online informal conversations. Our approach combines: (i) Wikipedia, an extensive source of knowledge, (ii) a concept association strategy, and (iii) a variety of text-mining techniques, such as POS tagging and named entities recognition. We performed a comparative evaluation procedure for searching the optimal combination of techniques, achieving encouraging results.Fil: Nicoletti, Matías Alberto. 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: 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
On the evaluation of community detection algorithms on heterogeneous social media data
No full textOne fundamental problem in social networks is the identification of groups of elements (also known as communities) when group membership is not explicitly available. Community detection has proven to be valuable in diverse domains such as biology, social sciences and bibliometrics. Thus, several community detection techniques have been developed. Nonetheless, as real networks are very heterogenous, the question of how communities should be assessed remains open. Whilst there are several works that have analysed the performance of diverse community detection algorithms over artificial graph benchmarks, the evaluation over real social networks has received comparatively less attention. Motivated by the lack of such studies, this chapter focuses on the analysis of the performance of community detection algorithms over social media networks, and the quantification of the structural properties of the discovered communities.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
Interface agents personalizing Web-based tasks
Full text linkThe volume of information available on the Web is constantly growing. Due to this situation, users looking for documents relevant to their interests need to identify them among all the available ones. Intelligent agents have become a solution to assist users in this task since they can retrieve, filter and organize information on behalf of their users. In this paper we present two experiences in the development of interface agents assisting users in Web-based tasks: PersonalSearcher, a personalized Web searcher, and NewsAgent, a personalized digital newspaper generator. The main challenge we faced to personalize the tasks carried out by these agents was learning and modeling specific and dynamic user interests. Our proposed approach consists of incrementally building a hierarchy of users' relevant topics and adapting it as agents interact with users over time.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: 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: 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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