An effective recommendation algorithm for clustering-based recommender systems

Taek Hun Kim; Sung Bong Yang

doi:10.1007/11589990_159

An effective recommendation algorithm for clustering-based recommender systems

Taek Hun Kim, Sung Bong Yang

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 Citations (Scopus)

Abstract

In this paper we present an effective recommendation algorithm using a refined neighbor selection and attributes information on the goods. The proposed algorithm exploits the transitivity of similarities using a graph approach. The algorithm also utilizes the attributes of the items. The experiment results show that the recommendation system with the proposed algorithm outperforms other systems and it can also overcome the very large-scale dataset problem without deteriorating prediction quality.

Original language	English
Title of host publication	AI 2005
Subtitle of host publication	Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings
Pages	1150-1153
Number of pages	4
DOIs	https://doi.org/10.1007/11589990_159
Publication status	Published - 2005
Event	18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence - Sydney, Australia Duration: 2005 Dec 5 → 2005 Dec 9

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3809 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence
Country	Australia
City	Sydney
Period	05/12/5 → 05/12/9

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/11589990_159

Fingerprint Dive into the research topics of 'An effective recommendation algorithm for clustering-based recommender systems'. Together they form a unique fingerprint.

Cite this

Kim, T. H., & Yang, S. B. (2005). An effective recommendation algorithm for clustering-based recommender systems. In AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings (pp. 1150-1153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3809 LNAI). https://doi.org/10.1007/11589990_159

Kim, Taek Hun ; Yang, Sung Bong. / An effective recommendation algorithm for clustering-based recommender systems. AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. 2005. pp. 1150-1153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{839e1ce18a8e4fa0afd6548a349f9078,

title = "An effective recommendation algorithm for clustering-based recommender systems",

abstract = "In this paper we present an effective recommendation algorithm using a refined neighbor selection and attributes information on the goods. The proposed algorithm exploits the transitivity of similarities using a graph approach. The algorithm also utilizes the attributes of the items. The experiment results show that the recommendation system with the proposed algorithm outperforms other systems and it can also overcome the very large-scale dataset problem without deteriorating prediction quality.",

author = "Kim, {Taek Hun} and Yang, {Sung Bong}",

note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; 18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence ; Conference date: 05-12-2005 Through 09-12-2005",

year = "2005",

doi = "10.1007/11589990_159",

language = "English",

isbn = "3540304622",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "1150--1153",

booktitle = "AI 2005",

}

Kim, TH & Yang, SB 2005, An effective recommendation algorithm for clustering-based recommender systems. in AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3809 LNAI, pp. 1150-1153, 18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence, Sydney, Australia, 05/12/5. https://doi.org/10.1007/11589990_159

An effective recommendation algorithm for clustering-based recommender systems. / Kim, Taek Hun; Yang, Sung Bong.

AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. 2005. p. 1150-1153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3809 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - An effective recommendation algorithm for clustering-based recommender systems

AU - Kim, Taek Hun

AU - Yang, Sung Bong

PY - 2005

Y1 - 2005

N2 - In this paper we present an effective recommendation algorithm using a refined neighbor selection and attributes information on the goods. The proposed algorithm exploits the transitivity of similarities using a graph approach. The algorithm also utilizes the attributes of the items. The experiment results show that the recommendation system with the proposed algorithm outperforms other systems and it can also overcome the very large-scale dataset problem without deteriorating prediction quality.

AB - In this paper we present an effective recommendation algorithm using a refined neighbor selection and attributes information on the goods. The proposed algorithm exploits the transitivity of similarities using a graph approach. The algorithm also utilizes the attributes of the items. The experiment results show that the recommendation system with the proposed algorithm outperforms other systems and it can also overcome the very large-scale dataset problem without deteriorating prediction quality.

UR - http://www.scopus.com/inward/record.url?scp=33745609807&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745609807&partnerID=8YFLogxK

U2 - 10.1007/11589990_159

DO - 10.1007/11589990_159

M3 - Conference contribution

AN - SCOPUS:33745609807

SN - 3540304622

SN - 9783540304623

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1150

EP - 1153

BT - AI 2005

T2 - 18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence

Y2 - 5 December 2005 through 9 December 2005

ER -

Kim TH, Yang SB. An effective recommendation algorithm for clustering-based recommender systems. In AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. 2005. p. 1150-1153. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11589990_159