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Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering
Joint Authors
Kwon, Hyeong-Joon
Hong, Kwang-Seok
Source
International Journal of Distributed Sensor Networks
Issue
Vol. 2013, Issue - (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-08-07
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Telecommunications Engineering
Information Technology and Computer Science
Abstract EN
Memory-based collaborative filtering selects the top-k neighbors with high rank similarity in order to predict a rating for an item that the target user has not yet experienced.
The most common traditional neighbor selection method for memory-based collaborative filtering is priority similarity.
In this paper, we analyze various problems with the traditional neighbor selection method and propose a novel method to improve upon them.
The proposed method minimizes the similarity evaluation errors with the existing neighbor selection method by considering the number of common items between two objects.
The method is effective for the practical application of collaborative filtering.
For validation, we analyze and compare experimental results between an existing method and the proposed method.
We were able to confirm that the proposed method can improve the prediction accuracy of memory-based collaborative filtering by neighbor selection that prioritizes the number of common items.
American Psychological Association (APA)
Kwon, Hyeong-Joon& Hong, Kwang-Seok. 2013. Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering. International Journal of Distributed Sensor Networks،Vol. 2013, no. -, pp.1-6.
https://search.emarefa.net/detail/BIM-503004
Modern Language Association (MLA)
Kwon, Hyeong-Joon& Hong, Kwang-Seok. Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering. International Journal of Distributed Sensor Networks Vol. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-503004
American Medical Association (AMA)
Kwon, Hyeong-Joon& Hong, Kwang-Seok. Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering. International Journal of Distributed Sensor Networks. 2013. Vol. 2013, no. -, pp.1-6.
https://search.emarefa.net/detail/BIM-503004
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-503004