Collaborative Filtering with q-Divergence-Based Relational Fuzzy c-Means Clustering
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- Kanzawa Yuchi
- Shibaura Institute of Technology
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- Atsuta Kaoru
- JFE Systems, Inc.
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- Midorikawa Genki
- PMO Nihonbashi Kayabacho
書誌事項
- タイトル別名
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- Collaborative Filtering with <i>q</i>-Divergence-Based Relational Fuzzy <i>c</i>-Means Clustering
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<p>This paper presents a collaborative filtering (CF) algorithm for a recommendation system motivated by the need for higher recommendation accuracy. Cluster analysis captures information from a group of users such that users within a given cluster are more similar to each other than to those in other clusters. Therefore, clustering helps to detect similar users. However, inconsistent similarity measures have been applied during the clustering and prediction stages in the literature. Hence, this study resolves such discrepancies through the proposed CF algorithm, which uses fuzzy clustering for relational data such that a common similarity measure is applied to both the clustering and prediction stages. Experiments were conducted with ten datasets based on an artificial dataset and 40 datasets based on eight real datasets to demonstrate that the proposed algorithm could achieve a higher CF accuracy than conventional methods.</p>
収録刊行物
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 27 (6), 1070-1078, 2023-11-20
富士技術出版株式会社
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詳細情報 詳細情報について
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- CRID
- 1390861150942781568
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- NII書誌ID
- AA12042502
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- ISSN
- 18838014
- 13430130
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- NDL書誌ID
- 033173029
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
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- 抄録ライセンスフラグ
- 使用不可