Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start Recommendation

Recommendation systems that use auxiliary information such as product names and categories have been proposed to address the cold-start problem. However, these methods do not perform well when we only have insufficient warm-start training data. On the other hand, large language models (LLMs) can perform as effective cold-start recommendation systems even with limited warm-start data. However, they require numerous API calls for inferences, which leads to high operational costs in terms of time and money. This is a significant concern in industrial applications. In this paper, we introduce a new method, RevAug, which leverages LLMs as a data augmentation to enhance cost-efficient cold-start recommendation systems. To generate pseudo-samples, we have reversed the commonly used prompt for an LLM from “Would this user like this item?” to “What kind of items would this user like?”. Generated outputs by this reverse prompt are pseudo-auxiliary information utilized to enhance recommendation systems in the training phase. In numerical experiments with four real-world datasets, RevAug demonstrated superior performance in cold-start settings with limited warm-start data compared to existing methods. Moreover, RevAug significantly reduced API fees and processing time compared to an LLM-based recommendation method.