Journal of the Korean Data and Information Science Society
- 제14권3호
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- Pages.441-450
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- 2003
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- 1598-9402(pISSN)
Improvement of Collaborative Filtering Algorithm Using Imputation Methods
초록
Collaborative filtering is one of the most widely used methodologies for recommendation system. Collaborative filtering is based on a data matrix of each customer's preferences and frequently, there exits missing data problem. We introduced two imputation approach (multiple imputation via Markov Chain Monte Carlo method and multiple imputation via bootstrap method) to improve the prediction performance of collaborative filtering and evaluated the performance using EachMovie data.