At large and popular conferences, it is not uncommon for attendees to feel overwhelmed and lost while trying to navigate through many parallel sessions. In this paper, we present a conference session recommender system. In contrast to the conventional »query-search» model where a system passively engages with users, Session Expert actively interacts with users via natural, human-like conversations and provides personalized recommendations. The underlying session recommender engine is designed to handle the cold start problem, and is lightweight to enable real-time session recommendations and rationale-aware response generation. Specifically, the recommender system alleviates the cold start problem by transferring knowledge from another similar conference in an offline setting. This step is achieved by first exploiting a positive-unlabeled (PU) learning model to reveal the underlying user interest from the historical enrollment data, and then modeling a bilinear relationship which captures how user and session features influence users' interests. Given the learned bilinear model, recommendation scores and rationale can be generated online as it only involves a few matrix-vector multiplications which can be computed efficiently.