AUGUST : an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets (2023)

Abstract: 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 datasets can hardly capture rich and large-scale cases in the real world, (ii) the limited experience and knowledge of annotators accounts for the uninformative corpus and inappropriate recommendations. In this paper, we propose a novel automatic dataset synthesis approach that can generate 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 developing a more effective conversational recommendation system.

## introduction conversational recommendation (cr) systems aim to recommend potential items of interest for users (or seekers) through dialogue-based interactions. although tremendous works have been contributed to the cr domain, the lack of both large-scale and high-quality training data remains a common problem due to the great cost and difficulty in dataset ## conclusion this paper proposes an automatic generation understudy for conversational recommendation datasets. by casting the dialogue synthesis process as a data2text generation task, a baseline framework is constructed to exploit (i) rich accurate user preferences from user-item matrices, (ii) rich external knowledge from external knowledge graphs, and (iii) the conversation ability from the corpus of existing cr datasets. experiment results show that our generation is comparable to human-labeled conversations and superior in scalability, extensibility, and explainability. more importantly, we empirically show the benefit of our synthesized data in improving a crs, especially in recommendation accuracy. the proposed approach exhibits great potential for automatic dataset synthesis and is expected to inspire researchers in other fields.