topic modeling | Academic Journals and Conferences

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

Features of Recommendation Algorithm on Base of Analysis of Social Network Data Mining Methods

In recent years, social media platforms have become powerful data collection tools to improve user experience. The vast amount of data generated through social media provides a unique opportunity to develop innovative recommendation systems. This article analyzes the application of data mining methods for social networks in the context of effective recommendation systems, focusing on three key methodologies: sentiment analysis (SA), topic modeling (TM) and social network analysis (SNA), highlighting their positive features.