  A conversational recommender system (CRS) is a practical application for item
recommendation through natural language conversation. Such a system estimates
user interests for appropriate personalized recommendations. Users sometimes
have various interests in different categories or genres, but existing studies
assume a unique user interest that can be covered by closely related items. In
this work, we propose to model such multiple user interests in CRS. We
investigated its effects in experiments using the ReDial dataset and found that
the proposed method can recommend a wider variety of items than that of the
baseline CR-Walker.
