Flexible and dynamic compromises for effective recommendations

Conversational Recommendation mimics the kind of dialog that takes between a customer and a shopkeeper involving multiple interactions where the user can give feedback at every interaction as opposed to Single Shot Retrieval, which corresponds to a scheme where the system retrieves a set of items in response to a user query in a single interaction. Compromise refers to a particular user preference which the recommender system failed to satisfy. But in the context of conversational systems, where the user's preferences keep on evolving as she interacts with the system, what constitutes as a compromise for her also keeps on changing. Typically, in Single Shot retrieval, the notion of compromise is characterized by the assignment of a particular feature to a particular dominance group such as MIB (higher value is better) or LIB (lower value is better) and this assignment remains true for all the users who use the system. In this paper, we propose a way to realize the notion of compromise in a conversational setting. Our approach, Flexi-Comp, introduces the notion of dynamically assigning a feature to two dominance groups simultaneously which is then used to redefine the notion of compromise. We show experimentally that a utility function based on this notion of compromise outperforms the existing conversational recommenders in terms of recommendation efficiency.