1. A recommendation apparatus, comprising: user base component extraction means for extracting a user base component selected according to a predetermined criterion from among components of user preference information forming a vector containing a plurality of components, the user preference information representing a preference of a user; item base component extraction means for extracting as an item base component a component corresponding to the user base component, from among components of item preference information formed as a vector, the vector containing components of the same number as the number of components of the user preference information, the component of the item preference information generated based on metadata information of each item serving as a target for recommendation; base similarity calculation means for calculating a base similarity, the base similarity being between a user base vector containing the user base components and an item base vector containing the item base components of the items serving as the targets for recommendation; item candidate identifying means for identifying, based on the base similarity, an item candidate to be recommended to the user; user inversion component extraction means for extracting, as a user inversion component, a component other than the user base component from among the components of the user preference information; item inversion component extraction means for extracting, as an item inversion component, a component corresponding to the user inversion component from among the components of the item preference information for the item candidates; inversion similarity calculation means for calculating an inversion similarity, the inversion similarity being between a user inversion vector containing the user inversion components and an item inversion vector containing the item inversion components of the items serving as the targets for recommendation; and recommendation item identifying means for identifying an item to be recommended to the user from among the item candidates based on the inversion similarity.