A recommender system suggests the items expected to be preferred by the users. Recommender systems use collaborative filtering (CF) to recommend items by summarizing the preferences of people who have tendencies similar to the user preference (see Figure). Traditional CF algorithms adopted the Semantic Differential (SD) method, in which preferences are measured using an n-point-scale on which extremes are represented by antonyms. We propose some CF algorithms adopting the ranking method. In the ranking method, the preferences are represented by orders, which are sorted item sequences according to the users' preferences. Our methods could recommend more preferable items to the users.