Multidimensional clustering based collaborative filtering approach for diversified recommendation

Xiaohui Li*, Tomohiro Murata

*この研究の対応する著者

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

This paper presents a hybrid recommendation approach that is used for discovering potential information with multidimensional clustering in recommender systems. This facilitates to obtain user groups for improving effectiveness and diversity of recommendation. The proposed algorithm works in three phases. In first phase, user groups are collected in the form of user profile, which applied multidimensional clustering algorithm and stored in the database for future recommendation. In second phase, the appropriate clusters are chosen using pruning of clusters. In third phase, the recommendations are generated for target user with similarity measures and quality rating prediction. The performance of proposed approach is evaluated using a public movie dataset and compared with two representative recommendation algorithms. The experimental results demonstrate that our proposed approach performs superiorly and alleviates problems, such as cold-start and data sparsity in collaborative filtering recommendation.

本文言語English
ホスト出版物のタイトルICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education
ページ905-910
ページ数6
DOI
出版ステータスPublished - 2012 11月 5
イベント2012 7th International Conference on Computer Science and Education, ICCSE 2012 - Melbourne, VIC, Australia
継続期間: 2012 7月 142012 7月 17

出版物シリーズ

名前ICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education

Conference

Conference2012 7th International Conference on Computer Science and Education, ICCSE 2012
国/地域Australia
CityMelbourne, VIC
Period12/7/1412/7/17

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)
  • 理論的コンピュータサイエンス

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