The speed with which new scientific articles are published and shared on academic social networks generated a situation of cognitive overload and the targeted access to the relevant information represents a major challenge for researchers. In this context, we propose a scientific article recommendation approach based on the discovery of thematic community structures, it focuses on the topological structure of the network combined with the analysis of the content of the social object (scientific article), a strategy that aims to mitigate the cold start problems and sparcity data in scoring matrix. A key element of our approach is the modeling of the researcher's thematic centers of interest derived from his corpus (a set of articles that interested him). In this perspective we use the technique of semantic exploration and extraction of latent topics in document corpora, LDA(Latent DirichletAllocation), an unsupervised learning method which offers the best solution of scalability problemcompared to other techniques of topic modeling. this technique allows us to build a profile model in the form of vectors in which the components are the probabilistic distributions on topics that reflect the interests of the researcher. The profile models thus constructed will be grouped into thematic clusters based on dominant topics using the fuzzy clustering algorithm, since the same topic can be treated in different scientific fields. Will follow a step of detection of community structures in thematic clusters to identify significant communities, the aim of this step is to project the recommendation process in a small space allowing better performance by reducing the computation time and the storage space for researcher/article data. The preliminary results of the experience of our approach on a population of 13 researchers and 60 articles shows that the articles generated by the recommendation process are very relevant to the target researcher or his community.