  Conversational Recommender System (CRS), which aims to recommend high-quality
items to users through interactive conversations, has gained great research
interest recently. A CRS is usually composed of a recommendation module and a
generation module. In the previous work, these two modules are loosely
connected in the model training and are shallowly integrated during inference,
where a simple switching or copy mechanism is adopted to incorporate
recommended items into generated responses. Moreover, the current end-to-end
neural models trained on small crowd-sourcing datasets (e.g., 10K dialogs in
the ReDial dataset) tend to overfit and have poor chit-chat ability. In this
work, we propose a novel unified framework that integrates recommendation into
the dialog (RecInDial) generation by introducing a vocabulary pointer. To
tackle the low-resource issue in CRS, we finetune the large-scale pretrained
language models to generate fluent and diverse responses, and introduce a
knowledge-aware bias learned from an entity-oriented knowledge graph to enhance
the recommendation performance. Furthermore, we propose to evaluate the CRS
models in an end-to-end manner, which can reflect the overall performance of
the entire system rather than the performance of individual modules, compared
to the separate evaluations of the two modules used in previous work.
Experiments on the benchmark dataset ReDial show our RecInDial model
significantly surpasses the state-of-the-art methods. More extensive analyses
show the effectiveness of our model.
