Abstract
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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Yongqing Wang received his BS degree in computer science from Zhejiang University of Technology in 2008 and MS degree in software engineering from Institute of Automation, Chinese Academy of Sciences in 2011. He is an engineer in Commercial Products Development Department at Alibaba (China) Co., Ltd. His research interests include ranking, information retrieval, and machine learning.
Wenji Mao received her PhD degree in computer science from the University of Southern California in 2006. She is an associate professor at the Institute of Automation, Chinese Academy of Sciences, a member of state key Laboratory of Management and Control for Complex System and a member of ACM and AAAI. Her research interests include artificial intelligence, intelligent agents and social computing.
Daniel Zeng received his PhD degree in industrial administration from Carnegie Mellon University in 1998. He is a professor at the Institute of Automation, Chinese Academy of Sciences. He is also affiliated with University of Arizona. He is also a member of the IEEE. His research interests include software agents and multi-agent systems, intelligence and security informatics, and recommender systems.
Fen Xia is a senior engineer in the Union Research and Development Department (URD) at Baidu (China) Co., Ltd. His research interests include statistical machine learning, ranking, large scale machine learning algorithms, regularization methods, and information retrieval.
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Wang, Y., Mao, W., Zeng, D. et al. Listwise approaches based on feature ranking discovery. Front. Comput. Sci. 6, 647–659 (2012). https://doi.org/10.1007/s11704-012-1170-7
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DOI: https://doi.org/10.1007/s11704-012-1170-7