Understanding Topical trends and user roles in topic evolution is an important challenge in the field of information retrieval. In this contribution, we present a novel model for analyzing evolution of user’s interests with respect to produced content over time. Our approach Author-Topic-Time model (ATT) addresses this problem by means of Bayesian modeling of relations between authors, latent topics and temporal information. We extend state of the art Latent Dirichlet Allocation (LDA) topic model to incorporate the author and timestamp information for capturing changes in user interest over time with respect to evolving latent topics. We present results of application of the model to the 9 years of scientific publication datasets from CiteSeer showing improved semantically cohesive topic detection and capturing shift in authors interest in relation to topic evolution. We also discuss opportunities of model use in novel mining and recommendation scenarios.