Collaborative compound critiquing

Abstract

Critiquing-based recommender systems offer users a conversational paradigm to provide their feedback, named critiques, during the process of viewing the current recommendation. In this way, the system is able to learn and adapt to the users’ preferences more precisely so that better recommendation could be returned in the subsequent iteration. Moreover, recent works on experience-based critiquing have suggested the power of improving the recommendation efficiency by making use of relevant sessions from other users’ histories so as to save the active user’s interaction effort. In this paper, we present a novel approach to processing the history data and apply it to the compound critiquing system. Specifically, we develop a history-aware collaborative compound critiquing method based on preference-based compound critique generation and graph-based similar session identification. Through experiments on two data sets, we validate the outperforming efficiency of our proposed method in comparison to the other experience-based methods. In addition, we verify that incorporating user histories into compound critiquing system can be significantly more effective than the corresponding unit critiquing system.