Recommender systems aim at suggesting items to users that fit their preferences. Collaborative filtering is one of the most popular approaches of recommender systems; it exploits users' ratings to express preferences. Traditional approaches of collaborative filtering suffer from the cold-start problem: when a new item enters the system, it cannot be recommended while a sufficiently high number of users have rated it. The quantity of required ratings is not known a priori and may be high as it depends on who rates the items. In this chapter, we propose to automatically select the adequate set of users in the network of users to address the cold-start problem. We call them the "delegates", and they correspond to those who should rate a new item first so as to reliably deduce the ratings of other users on this item. We propose to address this issue as an opinion poll problem. We consider two kinds of delegates: mentors and leaders. We experiment some measures, classically exploited in social networks, to select the adequate set of delegates. The experiments conducted show that only 6 delegates are sufficient to accurately estimate ratings of the whole set of other users, which dramatically reduces the number of users classically required.