Tagging has emerged as a powerful mechanism that enables users to ﬁnd, organize, and understand online entities. Recommender systems similarly enable users to efﬁciently navigate vast collections of items. Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the ﬂexibility and conceptual comprehensibility inherent in tagging systems. In this paper we explore tagommenders, recommender algorithms that predict users’ preferences for items based on their inferred preferences for tags. We describe tag preference inference algorithms based on users’ interactions with tags and movies, and evaluate these algorithms based on tag preference ratings collected from 995 MovieLens users. We design and evaluate algorithms that predict users’ ratings for movies based on their inferred tag preferences. Our tag-based algorithms generate better recommendation rankings than state-of-the-art algorithms, and they may lead to ﬂexible recommender systems that leverage the characteristics of items users ﬁnd most important.