Improving Conversational Recommendation Systems via Bias Analysis and
  Language-Model-Enhanced Data Augmentation
Xi Wang Hossein A. Rahmani Jiqun Liu Emine Yilmaz
Conversational Recommendation System (CRS) is a rapidly growing research area
that has gained significant attention alongside advancements in language
modelling techniques. However, the current state of conversational
recommendation faces numerous challenges due to its relative novelty and
limited existing contributions. In this study, we delve into benchmark datasets
for developing CRS models and address potential biases arising from the
feedback loop inherent in multi-turn interactions, including selection bias and
multiple popularity bias variants. Drawing inspiration from the success of
generative data via using language models and data augmentation techniques, we
present two novel strategies, 'Once-Aug' and 'PopNudge', to enhance model
performance while mitigating biases. Through extensive experiments on ReDial
and TG-ReDial benchmark datasets, we show a consistent improvement of CRS
techniques with our data augmentation approaches and offer additional insights
on addressing multiple newly formulated biases.