Improving the performance of unit critiquing

Conversational recommender systems allow users to learn and adapt their preferences according to concrete examples. Critiquing systems support such a conversational interaction style. Especially unit critiques offer a low cost feedback strategy for users in terms of the needed cognitive effort. In this paper we present an extension of the experience-based unit critiquing algorithm. The development of our new approach, which we call nearest neighbor compatibility critiquing, was aimed at increasing the efficiency of unit critiquing. We combine our new approach with existing critiquing strategies to ensemble-based variations and present the results of an empirical study that aimed at comparing the recommendation efficiency (in terms of the number of critiquing cycles) of ensemble-based solutions with individual critiquing algorithms.