  Conversational recommender systems (CRS) dynamically obtain the user
preferences via multi-turn questions and answers. The existing CRS solutions
are widely dominated by deep reinforcement learning algorithms. However, deep
reinforcement learning methods are often criticised for lacking
interpretability and requiring a large amount of training data to perform.
  In this paper, we explore a simpler alternative and propose a decision tree
based solution to CRS. The underlying challenge in CRS is that the same item
can be described differently by different users. We show that decision trees
are sufficient to characterize the interactions between users and items, and
solve the key challenges in multi-turn CRS: namely which questions to ask, how
to rank the candidate items, when to recommend, and how to handle negative
feedback on the recommendations. Firstly, the training of decision trees
enables us to find questions which effectively narrow down the search space.
Secondly, by learning embeddings for each item and tree nodes, the candidate
items can be ranked based on their similarity to the conversation context
encoded by the tree nodes. Thirdly, the diversity of items associated with each
tree node allows us to develop an early stopping strategy to decide when to
make recommendations. Fourthly, when the user rejects a recommendation, we
adaptively choose the next decision tree to improve subsequent questions and
recommendations. Extensive experiments on three publicly available benchmark
CRS datasets show that our approach provides significant improvement to the
state of the art CRS methods.
