Influence of Data-Derived Individualities on Persuasive Recommendation

Abstract

In this study, two machine learning based approaches have been compared that can add personal communication traits to a conversational recommender system. The first approach involves the creation of generative models for reactive tokens such as backchannels. The second approach involves a method for rewriting the conversational text by applying machine translation. Both approaches can impart personal communication traits to systems that incorporate a dialogue corpus. Two methods were implemented for a persuasive recommender system and their positive or negative effects based on an individual’s personality were experimentally analyzed through a restaurant ranking task. The results suggest that addition of personal communication traits decrease objective persuasiveness while increasing the individual’s impression on recommender systems.