Intention Recognition for Mixed-Initiative Recommender Systems

Recently ideas from mixed-initiative systems have been explored in the context of conversational recommender systems as a way to improve the interaction between the user and system. In this paper we examine some of the shortcomings of existing conversational recommender systems. In particular, we highlight how a more flexible recommendation strategy, one that responds to intermediate recommendation success and failures, can lead to significant improvements in both the efficiency and quality of recommendation dialogs. We argue that such techniques have a role to play in future mixedinitiative recommender systems.