SVM and collaborative filtering-based prediction of user preference for digital fashion recommendation systems

被引：4

作者：

Kang, Hanhoon ^{[1
]}

Yoo, Seong Joon ^{[1
]}

机构：

[1] Sejong Univ, Seoul 143747, South Korea

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2007年 / E90D卷 / 12期

关键词：

personalized search; recommendation; machine learning;

D O I：

10.1093/ietisy/e90-d.12.2100

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we describe a method of applying Collaborative Filtering with a Machine Learning technique to predict users' preferences for clothes on online shopping malls when user history is insufficient. In particular, we experiment with methods of predicting missing values, such as mean value, SVD, and support vector regression, to find the best method and to develop and utilize a unique feature vector model.

引用

页码：2100 / 2103

页数：4

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