How can we accurately predict for matter of taste using opinions from dissimilar individuals?

People frequently rely on the opinions of others to make decisions regarding matters of taste, such as choosing movies or restaurants. Then, how can we get accurate prediction from others' opinions? In this respect, similarity and majority-based strategies have been studied. However, the potential benefits of relying on dissimilar individuals' opinions remain unclear. This study investigates the efficacy of a novel strategy, termed the "Dissenting preference strategy". This strategy involves making a different (or opposite) choice from that of a person whose preferences differ from one's own. We used computer simulations based on the dataset comprising ratings from 14,000 individuals. Our results reveal that this strategy can improve decision-making accuracy, particularly when a moderate number of experiences (25 or more) and a certain neighborhood size (around 20 individuals or more) are available. However, the similarity-based strategy ("Doppelgänger strategy") consistently outperformed the Dissenting preference strategy. Nevertheless, further analyses highlighted that individuals with lower mean taste similarity could benefit more from the Dissenting preference strategy than from Doppelgänger strategy. Additionally, incorporating multiple dissimilar individuals (up to four) slightly enhanced the accuracy of the strategy. These findings underscore the conditional utility of dissimilar people in decision-making and suggest avenues for integrating dissimilarity into recommendation systems, especially for users with atypical preferences.