Personalizing and Improving Resource Recommendation by Analyzing Users Preferences in Social Tagging Activities

Collaborative tagging which is the keystone of the social practices of web 2.0 has been highly developed in the last few years. In this paper, we propose a new method to analyze user profiles according to their tagging activity in order to improve resource recommendation. We base upon association rules which is a powerful method to discover interesting relationships among large datasets on the web. Focusing on association rules we can find correlations between tags in a social network. Our aim is to recommend resources annotated with tags suggested by association rules, in order to enrich user profiles. The effectiveness of the recommendation depends on the resolution of social tagging drawbacks. In our recommender process, we demonstrate how we can reduce tag ambiguity and spelling variations problems by taking into account social similarities calculated on folksonomies, in order to personalize resource recommendation. We surmount also the lack of semantic links between tags during the recommendation process. Experiments are carried out with two different scenarios: the first one is a proof of concept over two baseline datasets and the second one is a real world application for diabetes disease

Construção colaborativa: estudo do emprego da Folksonomia em sistemas e-learning

Dissertação apresentada ao Programa de Pós-graduação em Comunicação da Universidade Municipal de São Caetano do SulEssa dissertação de mestrado se dedica ao estudo da utilização da Folksonomia no processo de classificação colaborativa e agregador de conteúdos baseados no sistema e-Folks e como meio de comunicação para auxiliar no aprendizado em sistemas e-Learning para facilitar a recuperação de informações no processo de aprendizagem do estudante. A Folksonomia potencializa as escolhas mais interessantes dos estudantes e pode indicar conteúdos mais adequados como subsídio para a aprendizagem. Seu emprego inovador em associação aos processos de comunicação de docentes foi discutido e uma análise das necessidades dos ambientes virtuais de aprendizagem em relação com as possibilidades de agregação de conteúdos por meio da etiquetagem recorrente se mostra como uma técnica importante em termos educacionais. O estudo exploratório aponta para potenciais e limitações do emprego da Folksonomia neste contexto, em função dos testes com a ferramenta e-Folks, construída para a sua aplicação e estudos. Os resultados indicam uma rápida curva de aprendizagem pelos alunos de duas turmas do curso técnico participantes do experimento e que a agregação coletiva de conteúdos pode auxiliar os estudantes em sua aprendizage

Reusing Ontologies to Enrich Semantically User Content in Web2.0: A Case Study on Folksonomies

Semantic Web and Web2.0 emerged during the past decade promising to achieve new frontiers for the Web. On the one hand, the Semantic Web is an interlinked web of data, supported by ontological semantics and allowing for intelligent applications such as semantic search and integration of heterogeneous content across systems and applications. On the other hand, Web2.0 represents the new technologies and paradigms that revolutionised the user engagement in content creation and introduced novel means towards social interaction. Bridging the gap between Web2.0 and the Semantic Web has been proposed as a means to better manage and interact with the large amounts of user contributed content, which is a new challenge for Web2.0. This thesis focuses on a popular paradigm of Web2.0, folksonomies. In particular, we investigate the semantic enrichment of folksonomy tagspaces by reusing ontologies available in the Semantic Web. We identify the need for methods that automatically apply semantic descriptions to user generated content without requiring user intervention or alteration of the current tagging paradigm. We use an iterative approach in order to identify the characteristics of folksonomies and the attributes of knowledge sources that influence the semantic enrichment of tagspaces. We build on the results of our experimental studies to implement a folksonomy enrichment algorithm, that given an input tagspace, automatically creates a semantic structure that describes the meaning and relations of tags. We introduce measures for the evaluation of enriched tagspaces and finally, we propose a search algorithm that exploits the semantic structures to improve folksonomy search

Using Data Mining for Facilitating User Contributions in the Social Semantic Web

This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and for recommendation of semantic relations. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on those systems