  We propose a recommendation system that suggests treatment strategies to a
therapist during the psychotherapy session in real-time. Our system uses a
turn-level rating mechanism that predicts the therapeutic outcome by computing
a similarity score between the deep embedding of a scoring inventory, and the
current sentence that the patient is speaking. The system automatically
transcribes a continuous audio stream and separates it into turns of the
patient and of the therapist and perform real-time inference of their
therapeutic working alliance. The dialogue pairs along with their computed
working alliance as ratings are then fed into a deep reinforcement learning
recommendation system where the sessions are treated as users and the topics
are treated as items. Other than evaluating the empirical advantages of the
core components on an existing dataset of psychotherapy sessions, we
demonstrate the effectiveness of this system in a web app.
