Recommender systems for social media have attracted considerable attentions due to its inherent features, such as a huge amount of information, social networks, and real-time features. In microblogs, which have been recognized as one of the most popular social media, most of URLs posted by users are considered to be fresh (i.e., shortly after creation). Hence, it is important to recommend URLs in microblogs for appropriate users because users become able to obtain such fresh URLs immediately. In this paper, we propose a URL recommender system using Twitter user lists. Twitter user list is the official functionality to group users into a list along with the name of it. Since it is expected that the members of a list (i.e., users included in the list) have similar characteristics, we utilize this feature to capture the user interests. Experimental results show that our proposed method achieves higher effectiveness than other methods based on the follow-followed network which does not offer user interests explicitly.