Multidimensional clustering based collaborative filtering approach for diversified recommendation

Xiaohui Li, Tomohiro Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education
Pages905-910
Number of pages6
DOIs
Publication statusPublished - 2012 Nov 5
Event2012 7th International Conference on Computer Science and Education, ICCSE 2012 - Melbourne, VIC, Australia
Duration: 2012 Jul 142012 Jul 17

Publication series

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

Conference

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

Keywords

  • clustering
  • collaborative filtering
  • multidimensional data
  • recommender systems

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Theoretical Computer Science

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  • Cite this

    Li, X., & Murata, T. (2012). Multidimensional clustering based collaborative filtering approach for diversified recommendation. In ICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education (pp. 905-910). [6295214] (ICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education). https://doi.org/10.1109/ICCSE.2012.6295214