@inproceedings{fa6a78dc8f2a4692a78a7578b95ffcdf,
title = "Comparison of fuzzy co-clustering methods in collaborative filtering-based recommender system",
abstract = "Various fuzzy co-clustering methods have been proposed for collaborative filtering; however, it is not clear which method is best in terms of accuracy. This paper proposes a recommender system that utilizes fuzzy co-clustering-based collaborative filtering and also evaluates four fuzzy co-clustering methods. The proposed system recommends optimal items to users using large-scale rating datasets. The results of numerical experiments conducted using one artificial dataset and two real datasets indicate that, the proposed method combined with a particular fuzzy co-clustering method is more accurate than conventional methods.",
keywords = "Co-clustering, Collaborative filtering, Fuzzy clustering",
author = "Tadafumi Kondo and Yuchi Kanzawa",
note = "Funding Information: supported by JSPS KAKENHI Grant Number Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 14th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2017 ; Conference date: 18-10-2017 Through 20-10-2017",
year = "2017",
doi = "10.1007/978-3-319-67422-3_10",
language = "English",
isbn = "9783319674216",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "103--116",
editor = "Aoi Honda and Yasuo Narukawa and Vicenc Torra and Sozo Inoue",
booktitle = "Modeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings",
}