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 Dec 1
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

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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)).

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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

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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