MOVIE RECOMMENDATION WITH K-MEANS CLUSTERING AND SELF-ORGANIZING MAP METHODS

被引：0

作者：

Seo, Eugene ^{[1
]}

Choi, Ho-Jin ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Techonol, Dept Comp Sci, 119 Munjro, Yuseong 305732, Daejeon, South Korea

来源：

ICAART 2010: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1: ARTIFICIAL INTELLIGENCE | 2010年

关键词：

Recommendation system; Machine learning; K-means clustering; Self-organisation map;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recommendation System has been developed to offer users a personalized service. We apply K-means and Self-Organizing Map (SOM) methods for the recommendation system. We explain each method in movie recommendation, and compare their performance in the sense of prediction accuracy and learning time. Our experimental results with given Netflix movie datasets demonstrates how SOM performs better than K-means to give precise prediction of movie recommendation with discussion, but it needs to be solved for the overall time of computation.

引用

页码：385 / 390

页数：6

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