  Recent research focuses beyond recommendation accuracy, towards human factors
that influence the acceptance of recommendations, such as user satisfaction,
trust, transparency and sense of control.We present a generic interactive
recommender framework that can add interaction functionalities to
non-interactive recommender systems.We take advantage of dialogue systems to
interact with the user and we design a middleware layer to provide the
interaction functions, such as providing explanations for the recommendations,
managing users preferences learnt from dialogue, preference elicitation and
refining recommendations based on learnt preferences.
