Listwise approaches based on feature ranking discovery
Listwise approaches based on feature ranking discovery
Yongqing WANG1, Wenji MAO2(), Daniel ZENG2,3, Fen XIA4
1. Commercial Products Development Department, Alibaba Inc., Hangzhou 310052, China; 2. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; 3. Department of Management Information Systems, University of Arizona, Tucson, AZ 85721, USA; 4. Union Research and Development Department, Baidu Inc., Beijing 100085, China
Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BLFeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.
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