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Personalizing the settings for Cf-based recommender systems by Il Im; Byung Ho Kim is a scholarly article available to read on EtoBox.

What is Personalizing the settings for Cf-based recommender systems about?

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.

Author

Il Im; Byung Ho Kim

Publisher

ACM

Published

2010

Language

EN