From choosing a book to read to picking a restaurant, most choices people encounter are about "matters of taste" and thus no universal, objective criterion about the quality of the options is available. Tapping into the knowledge of other, similar individuals who have already experienced and evaluated some options---as harnessed by recommender system algorithms---helps people select good options for themselves. We map 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". Using computer simulations on a widely-used data set from the recommender systems literature, we show that experienced individuals can benefit from relying only on the opinions of seemingly similar people. Inexperienced individuals, in contrast, are often well-advised to pick the mainstream option (i.e., that with the highest average evaluation) even if there are inter-individual differences in taste.