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http://dx.doi.org/10.14400/JDC.2019.17.2.347

A Movie Recommendation System processing High-Dimensional Data with Fuzzy-AHP and Fuzzy Association Rules  

Oh, Jae-Taek (Department of Computer Science & Engineering, Kongju National University)
Lee, Sang-Yong (Division of Computer Science & Engineering, Kongju National University)
Publication Information
Journal of Digital Convergence / v.17, no.2, 2019 , pp. 347-353 More about this Journal
Abstract
Recent recommendation systems are developing toward the utilization of high-dimensional data. However, high-dimensional data can increase algorithm complexity by expanding dimensions and be lower the accuracy of recommended items. In addition, it can cause the problem of data sparsity and make it difficult to provide users with proper recommended items. This study proposed an algorithm that classify users' subjective data with objective criteria with fuzzy-AHP and make use of rules with repetitive patterns through fuzzy association rules. Trying to check how problems with high-dimensional data would be mitigated by the algorithm, we performed 5-fold cross validation according to the changing number of users. The results show that the algorithm-applied system recorded accuracy that was 12.5% higher than that of the fuzzy-AHP-applied system and mitigated the problem of data sparsity.
Keywords
High-dimensional Data; Data Sparsity; Fuzzy-AHP; Fuzzy Association Rules; Recommendation System;
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