Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm

Authors:
Qiang He;Jun Ma;Shuaiqiang Wang
Affiliations:
Shandong University, Jinan, China;Shandong University, Jinan, China;Texas State University, San Marcos, TX, USA
Venue:
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Year:
2010

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Abstract

One fundamental issue of learning to rank is the choice of loss function to be optimized. Although the evaluation measures used in Information Retrieval (IR) are ideal ones, in many cases they can't be used directly because they do not satisfy the smooth property needed in conventional machine learning algorithms. In this paper a new method named RankCSA is proposed, which tries to use IR evaluation measure directly. It employs the clonal selection algorithm to learn an effective ranking function by combining various evidences in IR. Experimental results on the LETOR benchmarh datasets demonstrate that RankCSA outperforms the baseline methods in terms of P@n, MAP and NDCG@n.