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http://dx.doi.org/10.9728/dcs.2018.19.4.731

Compare to Factorization Machines Learning and High-order Factorization Machines Learning for Recommend system  

Cho, Seong-Eun (Department of Big Data, Korea University Graduate School of Computer & Information Technology)
Publication Information
Journal of Digital Contents Society / v.19, no.4, 2018 , pp. 731-737 More about this Journal
Abstract
The recommendation system is actively researched for the purpose of suggesting information that users may be interested in in many fields such as contents, online commerce, social network, advertisement system, and the like. However, there are many recommendation systems that propose based on past preference data, and it is difficult to provide users with little or no data in the past. Therefore, interest in higher-order data analysis is increasing and Matrix Factorization is attracting attention. In this paper, we study and propose a comparison and replay of the Factorization Machines Leaning(FM) model which is attracting attention in the recommendation system and High-Order Factorization Machines Learning(HOFM) which is a high - dimensional data analysis.
Keywords
Factorization Machines Learning; High-Order Factorization Machines Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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