  Conversational Recommender Systems (CRSs) in E-commerce platforms aim to
recommend items to users via multiple conversational interactions.
Click-through rate (CTR) prediction models are commonly used for ranking
candidate items. However, most CRSs are suffer from the problem of data
scarcity and sparseness. To address this issue, we propose a novel
knowledge-enhanced deep cross network (K-DCN), a two-step (pretrain and
fine-tune) CTR prediction model to recommend items. We first construct a
billion-scale conversation knowledge graph (CKG) from information about users,
items and conversations, and then pretrain CKG by introducing knowledge graph
embedding method and graph convolution network to encode semantic and
structural information respectively.To make the CTR prediction model sensible
of current state of users and the relationship between dialogues and items, we
introduce user-state and dialogue-interaction representations based on
pre-trained CKG and propose K-DCN.In K-DCN, we fuse the user-state
representation, dialogue-interaction representation and other normal feature
representations via deep cross network, which will give the rank of candidate
items to be recommended.We experimentally prove that our proposal significantly
outperforms baselines and show it's real application in Alime.
