  Most conversational recommendation approaches are either not explainable, or
they require external user's knowledge for explaining or their explanations
cannot be applied in real time due to computational limitations. In this work,
we present a real time category based conversational recommendation approach,
which can provide concise explanations without prior user knowledge being
required. We first perform an explainable user model in the form of preferences
over the items' categories, and then use the category preferences to recommend
items. The user model is performed by applying a BERT-based neural architecture
on the conversation. Then, we translate the user model into item recommendation
scores using a Feed Forward Network. User preferences during the conversation
in our approach are represented by category vectors which are directly
interpretable. The experimental results on the real conversational
recommendation dataset ReDial demonstrate comparable performance to the
state-of-the-art, while our approach is explainable. We also show the potential
power of our framework by involving an oracle setting of category preference
prediction.
