Recommender systems are used with increasing frequency in a wide variety of domains ranging from e- commerce to tourism, healthcare and online learning. However, the interaction with these systems generally tends to be limited to shallow feedback, such as providing ratings or filtering. Allowing users to interact with the recommender systems in a conversational environment brings opportunities in which the preferences can effectively be elicited from the users while the users can feel more in control of the whole process. However, when the existing non-interactive recommender systems are considered, it may not be easy to build an interactive layer directly on top of them. This is because there is already a great deal of modelling and work invested in the underlying algorithm and the system itself. Enabling interaction could mean rebuilding the whole solution from scratch, as the current design may not be able to consume preferences and information learnt from the user interaction online. In this paper, we propose the Interactive Recommender Framework, which converts non-interactive recommender solutions to conversational recommenders. We demonstrate how Interactive Recommender Framework can successfully enable interactivity on top of non-interactive recommender systems by integrating it into two different recommender algorithms from literature, and validate our solution through offline simulation experiments and online user studies.