recommender systems

Topic Modeling for News Recommendations: Evaluating the Performance of LDA and BERTopic

Text analysis is an important component in the evolution of recommender systems, as it enables meaningful information to be extracted from vast amounts of textual data.  This study performs a comparative analysis of two main topic modeling techniques, Latent Dirichlet Allocation (LDA) and BERTopic in the context of news recommender systems.  Using a dataset of Moroccan news articles, we evaluate the ability of these models to generate coherent and interpretable topics.  Our results demonstrate that BERTopic outperforms LDA in terms of topic consistency and semantic rich

Information technologies of learning-centered tuition support as a global educational trend

The features of the modern understanding of education as a learning-centered result-oriented learning are described. The learning individualization takes into account the psycho-physical development of the person, its abilities and special educational requirements in order to ensure favorable conditions for the full development of the personality. The concepts of individual educational route of student-centered learning throughout life, the individual educational trajectory, and the information-communication tools for inclusive teaching were proposed.