High-quality data is essential for conversational recommendation systems and
serves as the cornerstone of the network architecture development and training
strategy design. Existing works contribute heavy human efforts to manually
labeling or designing and extending recommender dialogue templates. However,
they suffer from (i) the limited number of human annotators results in that
datasets can hardly capture rich and large-scale cases in the real world, (ii)
the limited experience and knowledge of annotators account for the
uninformative corpus and inappropriate recommendations. In this paper, we
propose a novel automatic dataset synthesis approach that can generate both
large-scale and high-quality recommendation dialogues through a data2text
generation process, where unstructured recommendation conversations are
generated from structured graphs based on user-item information from the real
world. In doing so, we comprehensively exploit: (i) rich personalized user
profiles from traditional recommendation datasets, (ii) rich external knowledge
from knowledge graphs, and (iii) the conversation ability contained in
human-to-human conversational recommendation datasets. Extensive experiments
validate the benefit brought by the automatically synthesized data under
low-resource scenarios and demonstrate the promising potential to facilitate
the development of a more effective conversational recommendation system.

本文提出一种用于生成大规模且高质量的对话式推荐数据集的方法，该方法结合了传统推荐数据集的个性化用户资料，知识图谱的外部知识和来自人与人对话式推荐数据集的对话能力，并证明该方法在低资源情况下可有效帮助发展更有效的对话式推荐系统。