Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 |
| Pages | 9-15 |
| Number of pages | 7 |
| Publication status | Published - 8 Jul 2009 |
| Event | 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 - Montreal, QC, Canada Duration: 8 Jul 2009 → 12 Jul 2009 |
Publication series
| 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.
Keywords
- Learning to rank, Particle swarm optimization, Ranking function, Swarm intelligence
ASJC Scopus subject areas
- Computer Science(all)
- Computational Theory and Mathematics
- Mathematics(all)
- Theoretical Computer Science
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Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009. 2009. p. 9-15 (Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -