Metric Learning to Rank

We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that rankings of data induced by distance from a query can be optimized against various ranking measures, such as AUC, Precision-at-k, MRR, MAP or NDCG. We demonstrate experimental results on standard classification data sets, and a large-scale online dating recommendation problem.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Brian McFee, Gert R. G. Lanckriet
• Keywords: ICML 2010, Machine Learning, Metric Learning, Metric Learning Algorithm, Structural Svm Framework