Conversational Recommender System and Large Language Model Are Made for
  Each Other in E-commerce Pre-sales Dialogue
Yuanxing Liu Wei-Nan Zhang Yifan Chen Yuchi Zhang Haopeng Bai Fan Feng Hengbin Cui Yongbin Li Wanxiang Che
E-commerce pre-sales dialogue aims to understand and elicit user needs and
preferences for the items they are seeking so as to provide appropriate
recommendations. Conversational recommender systems (CRSs) learn user
representation and provide accurate recommendations based on dialogue context,
but rely on external knowledge. Large language models (LLMs) generate responses
that mimic pre-sales dialogues after fine-tuning, but lack domain-specific
knowledge for accurate recommendations. Intuitively, the strengths of LLM and
CRS in E-commerce pre-sales dialogues are complementary, yet no previous work
has explored this. This paper investigates the effectiveness of combining LLM
and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods:
CRS assisting LLM and LLM assisting CRS. We conduct extensive experiments on a
real-world dataset of Ecommerce pre-sales dialogues. We analyze the impact of
two collaborative approaches with two CRSs and two LLMs on four tasks of
Ecommerce pre-sales dialogue. We find that collaborations between CRS and LLM
can be very effective in some cases.