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