  A personalized conversational sales agent could have much commercial
potential. E-commerce companies such as Amazon, eBay, JD, Alibaba etc. are
piloting such kind of agents with their users. However, the research on this
topic is very limited and existing solutions are either based on single round
adhoc search engine or traditional multi round dialog system. They usually only
utilize user inputs in the current session, ignoring users' long term
preferences. On the other hand, it is well known that sales conversion rate can
be greatly improved based on recommender systems, which learn user preferences
based on past purchasing behavior and optimize business oriented metrics such
as conversion rate or expected revenue. In this work, we propose to integrate
research in dialog systems and recommender systems into a novel and unified
deep reinforcement learning framework to build a personalized conversational
recommendation agent that optimizes a per session based utility function.
