In this talk, we introduce the framework that we use for building our personalized Question Answering system. In particular, we describe its personalization functionality in which we have proposed a new probabilistic scoring approach based on the topics of the question and candidate answers. First, a set of topics of interest to the user is learned based on a topic modeling approach such as Latent Dirichlet Allocation. Then, the similarity of questions asked by the user to the candidate answers, returned by the search engine, is estimated by calculating the probability of the candidate answer given the question. This similarity is used to re-rank the answers returned by the search engine. Our preliminary experiments show that the re-ranking highly increases the performance of the Question Answering system calculated based on accuracy and MRR (mean reciprocal rank). In this talk, we also introduce the other research topics that we are currently performing in this domain.