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Indexed by:期刊论文

Date of Publication:2019-11-01

Journal:EXPERT SYSTEMS WITH APPLICATIONS

Included Journals:SCIE、EI

Volume:133

Page Number:75-85

ISSN No.:0957-4174

Key Words:Learning to rank; Feature generation; Machine learning; Information retrieval

Abstract: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.