Conversational recommendation systems (CRSs) enable users to use natural language feedback to control their recommendations, overcoming many of the challenges of traditional recommendation systems. However, the practical adoption of CRSs remains limited due to a lack of rich and diverse conversational training data that pairs user utterances with recommendations. To address this problem, we introduce a new method to generate synthetic training data by transforming curated item collections, such as playlists or movie watch lists, into item-seeking conversations. First, we use a biased random walk to generate a sequence of slates, or sets of item recommendations; then, we use a language model to generate corresponding user utterances. We demonstrate our approach by generating a conversational music recommendation dataset with over one million conversations, which were found to be consistent with relevant recommendations by a crowdsourced evaluation. Using the synthetic data to train a CRS, we significantly outperform standard retrieval baselines in offline and online evaluations.

该研究提出使用偏置随机游走和语言模型生成合成的训练数据，解决了无法获得丰富多样的对话训练数据的问题，并使用该方法生成了超过一百万个对话音乐推荐数据集进行了在线离线评估。

使用随机游走和语言模型生成音乐推荐对话的合成数据