Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 |
| Seiten | 9-15 |
| Seitenumfang | 7 |
| Publikationsstatus | Veröffentlicht - 8 Juli 2009 |
| Veranstaltung | 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 - Montreal, QC, Kanada Dauer: 8 Juli 2009 → 12 Juli 2009 |
Publikationsreihe
| Name | Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 |
|---|
Abstract
This paper presents an approach to automatically optimize the retrieval quality of ranking functions. Taking a Swarm Intelligence perspective, we present a novel method, Swarm-Rank, which is well-founded in a Particle Swarm Optimization framework. SwarmRank learns a ranking function by optimizing the combination of various types of evidences such content and hyperlink features, while directly maximizing Mean Average Precision, a widely used evaluation measure in Information Retrieval. Experimental results on well-established Learning To Rank benchmark datasets show that our approach significantly outperformed standard approaches (i.e., BM25) that only use basic statistical information derived from documents collections, and is found to be competitive with Ranking SVM and RankBoost in the task of ranking relevant documents at the very top positions.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Theoretische Informatik und Mathematik
- Mathematik (insg.)
- Theoretische Informatik
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Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009. 2009. S. 9-15 (Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Swarming to rank for information retrieval
AU - Diaz-Aviles, Ernesto
AU - Nejdl, Wolfgang
AU - Schmidt-Thieme, Lars
PY - 2009/7/8
Y1 - 2009/7/8
N2 - This paper presents an approach to automatically optimize the retrieval quality of ranking functions. Taking a Swarm Intelligence perspective, we present a novel method, Swarm-Rank, which is well-founded in a Particle Swarm Optimization framework. SwarmRank learns a ranking function by optimizing the combination of various types of evidences such content and hyperlink features, while directly maximizing Mean Average Precision, a widely used evaluation measure in Information Retrieval. Experimental results on well-established Learning To Rank benchmark datasets show that our approach significantly outperformed standard approaches (i.e., BM25) that only use basic statistical information derived from documents collections, and is found to be competitive with Ranking SVM and RankBoost in the task of ranking relevant documents at the very top positions.
AB - This paper presents an approach to automatically optimize the retrieval quality of ranking functions. Taking a Swarm Intelligence perspective, we present a novel method, Swarm-Rank, which is well-founded in a Particle Swarm Optimization framework. SwarmRank learns a ranking function by optimizing the combination of various types of evidences such content and hyperlink features, while directly maximizing Mean Average Precision, a widely used evaluation measure in Information Retrieval. Experimental results on well-established Learning To Rank benchmark datasets show that our approach significantly outperformed standard approaches (i.e., BM25) that only use basic statistical information derived from documents collections, and is found to be competitive with Ranking SVM and RankBoost in the task of ranking relevant documents at the very top positions.
KW - Learning to rank
KW - Particle swarm optimization
KW - Ranking function
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=72749119039&partnerID=8YFLogxK
U2 - 10.1145/1569901.1569904
DO - 10.1145/1569901.1569904
M3 - Conference contribution
AN - SCOPUS:72749119039
SN - 9781605583259
T3 - Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
SP - 9
EP - 15
BT - Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
T2 - 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Y2 - 8 July 2009 through 12 July 2009
ER -