Towards a taxonomy of user feedback intents for conversational recommendations

Understanding users’ feedback on recommendation in natural language is crucially important for assisting the system to refine its understanding of the user’s preferences and provide more accurate recommendations in the subsequent interactions. In this paper, we report the results of an exploratory study on a human-human dialogue dataset centered around movie recommendations. In particular, we manually labeled a set of over 200 dialogues at the utterance level, and then conducted descriptive analysis on them from both seekers’ and recommenders’ perspectives. The results reveal not only seekers’ feedback intents as well as the types of preferences they have expressed, but also the reactions of human recommenders that have finally led to successful recommendation. A taxonomy for feedback intents is established along with the results, which could be constructive for improving conversational recommender systems.