In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge when developing a sociable recommendation dialog system, due to the lack of dialog dataset annotated with such sociable strategies. Therefore, we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs. Our analysis shows that sociable recommendation strategies, such as sharing personal opinions or communicating with encouragement, more frequently lead to successful recommendations. Based on our dataset, we train end-to-end recommendation dialog systems with and without our strategy labels. In both automatic and human evaluation, our model with strategy incorporation outperforms the baseline model. This work is a first step for building sociable recommendation dialog systems with a basis of social science theories.

本研究针对推荐交互中缺乏注释的社交策略问题，提出了INSPIRED数据集，并设计了一种基于社会科学理论的注释方案，分析表明，推荐对话系统中采用社交策略，如个人观点分享等，更有可能成功推荐，而结合该策略的模型表现更好。这是建立基于社会科学理论的社交推荐对话系统的第一步。

INSPIRED: 面向社交化推荐对话系统