Critics  and  recommender  systems  mostly  rival  each  other:  Both  influence  people’s  choices  of,  say, films, music, restaurants or wines.  However, little is known about how the ratings of professional critics and amateurs compare and how they could be combined.  To address these questions, we created a new collaborative filtering dataset, with ratings for wine labels from both renowned wine critics’ and regular wine consumers’ (amateurs), and used it to simulate the performance of a standard collaborative filtering algorithm.  We studied how the k-nearest neighbor algorithm (k-nn) performs (both at the individual and aggregate level) when advice is drawn from critics and/or amateurs. We also formalized and visualized the social network spanned by k-nn by calculating how much a user is consulted by k-nn (potential influence) and how much a user can actually contribute to recommendations (i.e., has rated the target item; actual influence). We find that a system using both professional critics’ and amateurs’ ratings can substantially outperform systems relying on either of these groups alone.  And even though there is strong evidence of taste homophily between professional critics and amateurs (i.e., critics should get advice from critics and  amateurs  from  amateurs),  critics  exert  more  influence  in  the  actual  recommendations  because  they are more prolific raters.  Our results provide a proof of concept for how critics’ and amateurs’ opinions can be harnessed to build robust recommender systems for wines,  while our methods can be leveraged more generically to (i) make the recommendation process more transparent, (ii) identify influential users in recommender systems and (iii) investigate taste homophily in recommender networks and beyond.