ALLEVIATING THE SPARSITY PROBLEM OF COLLABORATIVE FILTERING USING AN EFFICIENT ITERATIVE CLUSTERED PREDICTION TECHNIQUE

Amira Abdelwahab (), Hiroo Sekiya, Ikuo Matsuba, Yasuo Horiuchi and Shingo Kuroiwa
Additional contact information
Amira Abdelwahab: Graduate School of Advanced Integration Science, Chiba University, Chiba, Inage-ku, 1-33 Yayoi-cho 263-8522, Japan
Hiroo Sekiya: Graduate School of Advanced Integration Science, Chiba University, Chiba, Inage-ku, 1-33 Yayoi-cho 263-8522, Japan
Ikuo Matsuba: Graduate School of Advanced Integration Science, Chiba University, Chiba, Inage-ku, 1-33 Yayoi-cho 263-8522, Japan
Yasuo Horiuchi: Graduate School of Advanced Integration Science, Chiba University, Chiba, Inage-ku, 1-33 Yayoi-cho 263-8522, Japan
Shingo Kuroiwa: Graduate School of Advanced Integration Science, Chiba University, Chiba, Inage-ku, 1-33 Yayoi-cho 263-8522, Japan

International Journal of Information Technology & Decision Making (IJITDM), 2012, vol. 11, issue 01, 33-53

Abstract: Collaborative filtering (CF) is one of the most prevalent recommendation techniques, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. Although CF has been widely applied in various applications, its applicability is restricted due to the data sparsity, the data inadequateness of new users and new items (cold start problem), and the growth of both the number of users and items in the database (scalability problem). In this paper, we propose an efficient iterative clustered prediction technique to transform user-item sparse matrix to a dense one and overcome the scalability problem. In this technique, spectral clustering algorithm is utilized to optimize the neighborhood selection and group the data into users' and items' clusters. Then, both clustered user-based and clustered item-based approaches are aggregated to efficiently predict the unknown ratings. Our experiments on MovieLens and book-crossing data sets indicate substantial and consistent improvements in recommendations accuracy compared to the hybrid user-based and item-based approach without clustering, hybrid approach withk-means and singular value decomposition (SVD)-based CF. Furthermore, we demonstrated the effectiveness of the proposed iterative technique and proved its performance through a varying number of iterations.

Keywords: Recommender system; iterative-based collaborative filtering; clustering-based collaborative filtering; memory-based collaborative filtering (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622012500022
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:11:y:2012:i:01:n:s0219622012500022

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622012500022

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().