FGFIREM: A feature generation framework based on information retrieval evaluation measures

作者：

Highlights：

• We improve AdaRank algorithm in terms of ERR, MRR and Q-measure.

• We propose a feature generation framework FGFIREM.

• We construct the feature sets based on the three kinds of ranking algorithms.

摘要

•We improve AdaRank algorithm in terms of ERR, MRR and Q-measure.•We propose a feature generation framework FGFIREM.•We construct the feature sets based on the three kinds of ranking algorithms.

论文关键词：Learning to rank,Feature generation,Machine learning,Information retrieval

论文评审过程：Received 18 October 2018, Revised 7 May 2019, Accepted 7 May 2019, Available online 8 May 2019, Version of Record 18 May 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.004