AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets
This addresses the data bottleneck for researchers and developers in conversational recommendation systems, offering a scalable alternative to manual annotation, though it is incremental as it builds on existing data sources and generation techniques.
The paper tackles the problem of limited and low-quality data for conversational recommendation systems by proposing an automatic dataset synthesis approach that generates large-scale, high-quality recommendation dialogues from structured graphs, validated through extensive experiments under low-resource scenarios.
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.