1. A computer-implemented method for recommending one or more genres of digital content items to a user, the method comprising: receiving one or more user preferences that reflect at least one attribute associated with one or more of the digital content items; determining at least one user preference tag based on the at least one attribute, wherein the at least one user preference tag comprises metadata reflective of the at least one attribute; identifying a subset of the one or more genres included within a set of genres reflective of the one or more user preferences by comparing, for each genre in the set of genres, the at least one user preference tag to one or more of the plurality of metadata tags defining the genre, wherein, for each genre in the subset of the one or more genres, the at least one user preference tag matches one of the one or more of the plurality of metadata tags defining the genre; for each genre included in the subset of the one or more genres, computing a score for the genre based on the one or more user preferences and the one or more of the plurality of metadata tags defining the genre, wherein the step of computing the score for the genre comprises: computing an individual tag score for each of the plurality of metadata tags defining the genre, and combining a plurality of the individual tag scores of the plurality of metadata tags defining the genre; sorting the subset of genres based on the score associated with each genre included in the subset of genres; and selecting one or more genres from the sorted subset of genres to recommend to the user.