论文名称：FGFIREM: A feature generation framework based on information retrieval evaluation measures
论文类型：期刊论文
发表刊物：EXPERT SYSTEMS WITH APPLICATIONS
收录刊物：SCIE、EI
卷号：133
页面范围：75-85
ISSN号：0957-4174
关键字：Learning to rank; Feature generation; Machine learning; Information retrieval
摘要：Learning to rank has become one of the most popular research areas in recent years. A series of learning to rank algorithms have been proposed to improve the ranking performance. In this work, we propose three learning to rank algorithms by directly optimizing evaluation measures based on the AdaRank algorithms. We name the three algorithms as AdaRank-ERR, AdaRank-MRR and AdaRank-Q which optimize three evaluation measures, Expected Reciprocal Rank (ERR), Mean Reciprocal Rank (MRR), and Q-measure (Q), based on AdaRank, respectively. Furthermore, we propose a novel feature generation framework FG-FIREM to enhance the ranking performance. The framework generates effective document ranking features based on the ranking scores assigned by the proposed algorithms, and enriches the original feature space of learning to rank using the generated features for improving the ranking performance. We evaluate the proposed framework on three datasets from LETOR3.0 and the web dataset MSLR-WEB10K. The experimental results demonstrate that our framework can effectively improve the ranking performance. (C) 2019 Elsevier Ltd. All rights reserved.
发表时间：2019-11-01