Proceedings of the Annual Meeting of the Cognitive Science Society

From choosing a book to picking a restaurant, most choices people encounter are about “matters of taste” and thus no universal, objective criterion about the options’ quality exists. Tapping into the knowledge of individuals with similar tastes who have already experienced and evaluated options—as harnessed by recommender system algorithms—helps people select options that they will enjoy. Although recommender systems are available in some domains, for most everyday decisions there is neither an algorithm nor “big data” at hand. We mapped recommender system algorithms to models of human judgment and decision making about “matters of fact” and then recast the latter as social recommender strategies for “matters of taste”. This allowed us to investigate how people can leverage the experiences of other individuals to make better decisions when no machine recommender systems are available. Using computer simulations on a widely used data set from the recommender systems literature, we show that experienced individuals can benefit from relying on only the opinions of seemingly similar people. Inexperienced individuals, in contrast, are often well-advised to pick the mainstream option (i.e., the one with the highest average evaluation) even if there are interindividual differences in taste; this is because reliable estimation of similarity requires considerable experience