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 | |
| 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 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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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 - 2005/12/1
Y1 - 2005/12/1
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
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
ER -