Ratings by users on various items such as hotels and movies have become easily available on the Web. In many cases, other than overall rating for each item by each user, more detailed information such as ratings from different viewpoints and free text comments, as well as aggregated information such as the average of ratings by different users, are also available. We investigated the effectiveness of six existing collaborative filtering methods for large-scale sparse multicriteria rating data. We formulated rating aggregation as a collaborative filtering problem and applied six collaborative filtering methods to it. Furthermore, we extended three of the methods to calculate user similarity using indirect users and review comments and applied them to collaborative filtering and rating aggregation. The results show that multicriteria rating approaches perform better than single criterion rating approaches. The extended methods had better performance both in collaborative filtering and in rating aggregation. 2017 IEEE International Conference on Big Data(Big Data 2017) . December 11-14, 2017, Boston, MA, USA