抄録
This paper describes a hybrid recommendation approach for discovering individual users' potential preferences from multidimensional clustering view. The proposed approach aims to help users reach a decision to meet their diverse demands and provide the target user with highly idiosyncratic or more diverse recommendations. To this end, we propose a hybrid approach that incorporates multidimensional clustering into a collaborative filtering recommendation model to provide a quality recommendation. The proposed approach also provides a flexible solution for improving recommendation diversity and achieves a tradeoff between recommendation accuracy and diversity. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The empirical results demonstrate that our proposed approach performs superiorly on increasing recommendation diversity while maintaining recommendation accuracy.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 749-755 |
| ページ数 | 7 |
| ジャーナル | IEEJ Transactions on Electronics, Information and Systems |
| 巻 | 133 |
| 発行部数 | 4 |
| DOI | |
| 出版物ステータス | Published - 2013 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
これを引用
A hybrid method using multidimensional clustering-based collaborative filtering to improve recommendation diversity. / Li, Xiaohui; Murata, Tomohiro.
：: IEEJ Transactions on Electronics, Information and Systems, 巻 133, 番号 4, 2013, p. 749-755.研究成果: Article
}
TY - JOUR
T1 - A hybrid method using multidimensional clustering-based collaborative filtering to improve recommendation diversity
AU - Li, Xiaohui
AU - Murata, Tomohiro
PY - 2013
Y1 - 2013
N2 - This paper describes a hybrid recommendation approach for discovering individual users' potential preferences from multidimensional clustering view. The proposed approach aims to help users reach a decision to meet their diverse demands and provide the target user with highly idiosyncratic or more diverse recommendations. To this end, we propose a hybrid approach that incorporates multidimensional clustering into a collaborative filtering recommendation model to provide a quality recommendation. The proposed approach also provides a flexible solution for improving recommendation diversity and achieves a tradeoff between recommendation accuracy and diversity. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The empirical results demonstrate that our proposed approach performs superiorly on increasing recommendation diversity while maintaining recommendation accuracy.
AB - This paper describes a hybrid recommendation approach for discovering individual users' potential preferences from multidimensional clustering view. The proposed approach aims to help users reach a decision to meet their diverse demands and provide the target user with highly idiosyncratic or more diverse recommendations. To this end, we propose a hybrid approach that incorporates multidimensional clustering into a collaborative filtering recommendation model to provide a quality recommendation. The proposed approach also provides a flexible solution for improving recommendation diversity and achieves a tradeoff between recommendation accuracy and diversity. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The empirical results demonstrate that our proposed approach performs superiorly on increasing recommendation diversity while maintaining recommendation accuracy.
KW - Collaborative filtering
KW - Multidimensional clustering
KW - Recommendation diversity
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=84877786728&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877786728&partnerID=8YFLogxK
U2 - 10.1541/ieejeiss.133.749
DO - 10.1541/ieejeiss.133.749
M3 - Article
AN - SCOPUS:84877786728
VL - 133
SP - 749
EP - 755
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
SN - 0385-4221
IS - 4
ER -