  Conversational recommender systems (CRSs) aim to provide recommendation
services via natural language conversations. Although a number of approaches
have been proposed for developing capable CRSs, they typically rely on
sufficient training data for training. Since it is difficult to annotate
recommendation-oriented dialogue datasets, existing CRS approaches often suffer
from the issue of insufficient training due to the scarcity of training data.
To address this issue, in this paper, we propose a CounterFactual data
simulation approach for CRS, named CFCRS, to alleviate the issue of data
scarcity in CRSs. Our approach is developed based on the framework of
counterfactual data augmentation, which gradually incorporates the rewriting to
the user preference from a real dialogue without interfering with the entire
conversation flow. To develop our approach, we characterize user preference and
organize the conversation flow by the entities involved in the dialogue, and
design a multi-stage recommendation dialogue simulator based on a conversation
flow language model. Under the guidance of the learned user preference and
dialogue schema, the flow language model can produce reasonable, coherent
conversation flows, which can be further realized into complete dialogues.
Based on the simulator, we perform the intervention at the representations of
the interacted entities of target users, and design an adversarial training
method with a curriculum schedule that can gradually optimize the data
augmentation strategy. Extensive experiments show that our approach can
consistently boost the performance of several competitive CRSs, and outperform
other data augmentation methods, especially when the training data is limited.
Our code is publicly available at https://github.com/RUCAIBox/CFCRS.
