IEICE TRANSACTIONS on Information
A New Evolutionary Approach to Recommender Systems
Summary :
In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.3 pp.622-625
- Publication Date
- 2014/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.622
- Type of Manuscript
- LETTER
- Category
- Biocybernetics, Neurocomputing
Authors
Hyun-Tae KIM
Sungkyunkwan University
Jinung AN
DGIST
Chang Wook AHN
Sungkyunkwan University
Keyword
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Hyun-Tae KIM, Jinung AN, Chang Wook AHN, "A New Evolutionary Approach to Recommender Systems" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 3, pp. 622-625, March 2014, doi: 10.1587/transinf.E97.D.622.
Abstract: In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.622/_p
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@ARTICLE{e97-d_3_622,
author={Hyun-Tae KIM, Jinung AN, Chang Wook AHN, },
journal={IEICE TRANSACTIONS on Information},
title={A New Evolutionary Approach to Recommender Systems},
year={2014},
volume={E97-D},
number={3},
pages={622-625},
abstract={In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.},
keywords={},
doi={10.1587/transinf.E97.D.622},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - A New Evolutionary Approach to Recommender Systems
T2 - IEICE TRANSACTIONS on Information
SP - 622
EP - 625
AU - Hyun-Tae KIM
AU - Jinung AN
AU - Chang Wook AHN
PY - 2014
DO - 10.1587/transinf.E97.D.622
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2014
AB - In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.
ER -
English
