An Ontology Engineering Approach to User Profiling for Virtual Tours of Museums and Galleries

This paper describes a study of the development of a hierarchical ontology for producing and maintaining personalized profiles to improve the experience of visitors to virtual art galleries and museums. The paper begins by describing some of the features of virtual exhibitions and offers examples of virtual tours that the reader may wish to examine in more detail. The paper then discusses the ontology engineering (OE) approach and domain modelling languages (e.g. KACTUS, SENSUS and METHONTOLOGY). It then follows a basic OE approach to define classes for a cultural heritage virtual tour and to produce a Visitor Profile Ontology that is hierarchical and has static and dynamic elements. It concludes by suggesting ways in which the ontology may be automated to provide a richer, more immersive personalized visitor experience

An Ontology Engineering Approach to User Profiling for Virtual Tours of Museums and Galleries

This paper describes a study of the development of a hierarchical ontology for producing and maintaining personalized profiles to improve the experience of visitors to virtual art galleries and museums. The paper begins by describing some of the features of virtual exhibitions and offers examples of virtual tours that the reader may wish to examine in more detail. The paper then discusses the ontology engineering (OE) approach and domain modelling languages (e.g. KACTUS, SENSUS and METHONTOLOGY). It then follows a basic OE approach to define classes for a cultural heritage virtual tour and to produce a Visitor Profile Ontology that is hierarchical and has static and dynamic elements. It concludes by suggesting ways in which the ontology may be automated to provide a richer, more immersive personalized visitor experience

Resources and users in the tagging process: approaches and case studies

In this contribution we propose a comparison between two distinct approaches to the annotation of digital resources. The former, top-down, is rooted in the cathedral model and is based on an authoritative, centralized definition of the adopted mark-up language; the latter, bottom-up, refers to the bazaar model and is based on the contributions of a community of users. These two approaches are analyzed taking into account both their descriptive potential and the constraints they impose on the reasoning process of recommender systems, with special reference to user profiling. Three case studies are described, with reference to research projects that apply these approaches in the contexts of e-learning and knowledge management

User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page

Ambientes personalizados de e-learning: considerando os contextos dos alunos

A personalização em sistemas de e-learning é fundamental, uma vez que esses são utilizados por uma grande variedade de alunos, com características diferentes. Há várias abordagens que visam personalizar ambientes e- learning. No entanto, esses se concentram principalmen- te na tecnologia e / ou em detalhes da rede, sem levar em consideração os aspectos contextuais. Eles consideram apenas uma versão limitada do contexto, proporcionando personalização. Em nosso trabalho, o objetivo é melhorar a personalização do ambiente de aprendizagem e-learning, fazendo uso de uma melhor compreensão e modelagem do contexto educacional e tecnológico do usuário, utilizando ontologias. Mostramos um exemplo do uso da nossa pro- posta no sistema AdaptWeb, na qual o conteúdo e as re- comendações de navegação fornecidas dependem do con- texto do aluno

Using Semantic-Based User Profile Modeling for Context-Aware Personalised Place Recommendations

Place Recommendation Systems (PRS's) are used to recommend places to visit to World Wide Web users. Existing PRS's are still limited by several problems, some of which are the problem of recommending similar set of places to different users (Lack of Personalization) and no diversity in the set of recommended items (Content Overspecialization). One of the main objectives in the PRS's or Contextual suggestion systems is to fill the semantic gap among the queries and suggestions and going beyond keywords matching. To address these issues, in this study we attempt to build a personalized context-aware place recommender system using semantic-based user profile modeling to address the limitations of current user profile building techniques and to improve the retrieval performance of personalized place recommender system. This approach consists of building a place ontology based on the Open Directory Project (ODP), a hierarchical ontology scheme for organizing websites. We model a semantic user profile from the place concepts extracted from place ontology and weighted according to their semantic relatedness to user interests. The semantic user profile is then exploited to devise a personalized recommendation by re-ranking process of initial search results for improving retrieval performance. We evaluate this approach on dataset obtained using Google Paces API. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based place recommendation model

Personalized information retrieval based on context and ontological knowledge

The article has been accepted for publication and appeared in a revised form, subsequent to peer review and/or editorial input by Cambridge University PressExtended papers from C&O-2006, the second International Workshop on Contexts and Ontologies, Theory, Practice and Applications1 collocated with the seventeenth European Conference on Artificial Intelligence (ECAI)Context modeling has been long acknowledged as a key aspect in a wide variety of problem domains. In this paper we focus on the combination of contextualization and personalization methods to improve the performance of personalized information retrieval. The key aspects in our proposed approach are a) the explicit distinction between historic user context and live user context, b) the use of ontology-driven representations of the domain of discourse, as a common, enriched representational ground for content meaning, user interests, and contextual conditions, enabling the definition of effective means to relate the three of them, and c) the introduction of fuzzy representations as an instrument to properly handle the uncertainty and imprecision involved in the automatic interpretation of meanings, user attention, and user wishes. Based on a formal grounding at the representational level, we propose methods for the automatic extraction of persistent semantic user preferences, and live, ad-hoc user interests, which are combined in order to improve the accuracy and reliability of personalization for retrieval.This research was partially supported by the European Commission under contracts FP6-001765 aceMedia and FP6-027685 MESH. The expressed content is the view of the authors but not necessarily the view of the aceMedia or MESH projects as a whole