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http://dx.doi.org/10.15207/JKCS.2019.10.11.159

A Study of Similarity Measure Algorithms for Recomendation System about the PET Food  

Kim, Sam-Taek (School of Information Technology Convergence, Woosong University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.11, 2019 , pp. 159-164 More about this Journal
Abstract
Recent developments in ICT technology have increased interest in the care and health of pets such as dogs and cats. In this paper, cluster analysis was performed based on the component data of pet food to be used in various fields of the pet industry. For cluster analysis, the similarity was analyzed by analyzing the correlation between components of 300 dogs and cats in the market. In this paper, clustering techniques such as Hierarchical, K-Means, Partitioning around medoids (PAM), Density-based, Mean-Shift are clustered and analyzed. We also propose a personalized recommendation system for pets. The results of this paper can be used for personalized services such as feed recommendation system for pets.
Keywords
Big data; Cluster Analysis; Pet; PET food; Recommendation system; Similarity measure;
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Times Cited By KSCI : 6  (Citation Analysis)
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