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http://dx.doi.org/10.5351/KJAS.2020.36.6.833

Hierarchical grouping recommendation system based on the attributes of contents: a case study of 'The Movie Dataset'  

Kim, Yoon Kyoung (Department of Statistics, Sookmyung Women's University)
Yeo, In-Kwon (Department of Statistics, Sookmyung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.6, 2020 , pp. 833-842 More about this Journal
Abstract
Global platforms such as Netflix, Amazon, and YouTube have developed a precise recommendation system based on various information from large set of customers and many of the items recommended here are leading to actual purchases. In this paper, a cluster analysis was conducted according to the attribute of the content, expecting that there would be a difference in user preferences according to the attribute of the recommended content. Gower distance was used for use regardless of the type of variables. In this paper, using the data of movie rating site 'The Movie Dataset', the users were grouped hierarchically and recommended movies based on genre, director and actor variables. To evaluate the recommended systems proposed, user group was divided into train set and test set to examine the precision. The results showed that proposed algorithms have far higher precision than UBCF.
Keywords
clustering; Gower's distance; precision;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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