A recommender system suggests the items expected to be preferred by the users. Recommender systems use collaborative filtering to recommend items by summarizing the preferences of people who have tendencies simliar to the user preference. Traditionally, the degree of preference is represented by a scale, for example, one that ranges from one to five. This type of measuring technique is called the semantic differential (SD) method. We adopted the ranking method, however, rather than the SD method, since the SD method is intrinsically not suited for representing individual preferences. In the ranking method, the preferences are represented by orders, which are sorted item sequences according to the users' preferences. We here propose some methods to recommend items based on these order responses, and carry out the comparison experiments of these methods. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--Clustering, Infor...