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GECCO
2009
Springer

Swarming to rank for information retrieval

10 years 8 months ago
Swarming to rank for information retrieval
This paper presents an approach to automatically optimize the retrieval quality of ranking functions. Taking a Swarm Intelligence perspective, we present a novel method, SwarmRank, 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. Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence—Multiagent systems; I.2.6 [Ar...
Ernesto Diaz-Aviles, Wolfgang Nejdl, Lars Schmidt-
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where GECCO
Authors Ernesto Diaz-Aviles, Wolfgang Nejdl, Lars Schmidt-Thieme
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