An Improved Neighbor Selection Algorithm in Collaborative Filtering
Summary :
Nowadays, customers spend much time and effort in finding the best suitable goods since more and more information is placed on-line. To save their time and effort in searching the goods they want, a customized recommender system is required. In this paper we present an improved neighbor selection algorithm that exploits a graph approach. The graph approach allows us to exploit the transitivity of similarities. The algorithm searches more efficiently for set of influential customers with respect to a given customer. We compare the proposed recommendation algorithm with other neighbor selection methods. The experimental results show that the proposed algorithm outperforms other methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.5 pp.1072-1076
- Publication Date
- 2005/05/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.5.1072
- Type of Manuscript
- LETTER
- Category
- Contents Technology and Web Information Systems
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Taek-Hun KIM, Sung-Bong YANG, "An Improved Neighbor Selection Algorithm in Collaborative Filtering" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 5, pp. 1072-1076, May 2005, doi: 10.1093/ietisy/e88-d.5.1072.
Abstract: Nowadays, customers spend much time and effort in finding the best suitable goods since more and more information is placed on-line. To save their time and effort in searching the goods they want, a customized recommender system is required. In this paper we present an improved neighbor selection algorithm that exploits a graph approach. The graph approach allows us to exploit the transitivity of similarities. The algorithm searches more efficiently for set of influential customers with respect to a given customer. We compare the proposed recommendation algorithm with other neighbor selection methods. The experimental results show that the proposed algorithm outperforms other methods.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.5.1072/_p
Copy
@ARTICLE{e88-d_5_1072,
author={Taek-Hun KIM, Sung-Bong YANG, },
journal={IEICE TRANSACTIONS on Information},
title={An Improved Neighbor Selection Algorithm in Collaborative Filtering},
year={2005},
volume={E88-D},
number={5},
pages={1072-1076},
abstract={Nowadays, customers spend much time and effort in finding the best suitable goods since more and more information is placed on-line. To save their time and effort in searching the goods they want, a customized recommender system is required. In this paper we present an improved neighbor selection algorithm that exploits a graph approach. The graph approach allows us to exploit the transitivity of similarities. The algorithm searches more efficiently for set of influential customers with respect to a given customer. We compare the proposed recommendation algorithm with other neighbor selection methods. The experimental results show that the proposed algorithm outperforms other methods.},
keywords={},
doi={10.1093/ietisy/e88-d.5.1072},
ISSN={},
month={May},}
Copy
TY - JOUR
TI - An Improved Neighbor Selection Algorithm in Collaborative Filtering
T2 - IEICE TRANSACTIONS on Information
SP - 1072
EP - 1076
AU - Taek-Hun KIM
AU - Sung-Bong YANG
PY - 2005
DO - 10.1093/ietisy/e88-d.5.1072
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2005
AB - Nowadays, customers spend much time and effort in finding the best suitable goods since more and more information is placed on-line. To save their time and effort in searching the goods they want, a customized recommender system is required. In this paper we present an improved neighbor selection algorithm that exploits a graph approach. The graph approach allows us to exploit the transitivity of similarities. The algorithm searches more efficiently for set of influential customers with respect to a given customer. We compare the proposed recommendation algorithm with other neighbor selection methods. The experimental results show that the proposed algorithm outperforms other methods.
ER -
English
