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http://dx.doi.org/10.13088/jiis.2012.18.2.131

The Effect of the Personalized Settings for CF-Based Recommender Systems  

Im, Il (School of Business, Yonsei University)
Kim, Byung-Ho (School of Business, Yonsei University)
Publication Information
Journal of Intelligence and Information Systems / v.18, no.2, 2012 , pp. 131-141 More about this Journal
Abstract
In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with 'training' and 'verification' datasets. The method is compared with traditional 'constant settings' methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.
Keywords
Collaborative filtering; Personalization; Netflix;
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Times Cited By KSCI : 2  (Citation Analysis)
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