SVM and Collaborative Filtering-Based Prediction of User Preference for Digital Fashion Recommendation Systems
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.12 pp.2100-2103
- Publication Date
- 2007/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.12.2100
- Type of Manuscript
- LETTER
- Category
- Data Mining
Authors
Keyword
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Hanhoon KANG, Seong Joon YOO, "SVM and Collaborative Filtering-Based Prediction of User Preference for Digital Fashion Recommendation Systems" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 12, pp. 2100-2103, December 2007, doi: 10.1093/ietisy/e90-d.12.2100.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.12.2100/_p
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@ARTICLE{e90-d_12_2100,
author={Hanhoon KANG, Seong Joon YOO, },
journal={IEICE TRANSACTIONS on Information},
title={SVM and Collaborative Filtering-Based Prediction of User Preference for Digital Fashion Recommendation Systems},
year={2007},
volume={E90-D},
number={12},
pages={2100-2103},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e90-d.12.2100},
ISSN={1745-1361},
month={December},}
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TY - JOUR
TI - SVM and Collaborative Filtering-Based Prediction of User Preference for Digital Fashion Recommendation Systems
T2 - IEICE TRANSACTIONS on Information
SP - 2100
EP - 2103
AU - Hanhoon KANG
AU - Seong Joon YOO
PY - 2007
DO - 10.1093/ietisy/e90-d.12.2100
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2007
AB - 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.
ER -
English
