Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
This paper presents an overview of learning to rank. It includes three parts: related concepts including the definitions of ranking and learning to rank; a summary of pointwise models, pairwise models, and listwise models; estimation measures such as Normalized Discount Cumulative Gain and Mean Average Precision, respectively. Considering the deficiency that current learning to rank models lack of continual learning ability, we present a new continual learning idea that combines multi-agent autonomy learning mechanism with molecular immune mechanism for ranking.
Xishuang Dong, Xiaodong Chen, Sheng Li, Yi Guan, Zhiming Yu, "An Overview of Learning to Rank for Information Retrieval", Computer Science and Information Engineering, World Congress on, vol. 03, no. , pp. 600-606, 2009, doi:10.1109/CSIE.2009.1090