TY - GEN
T1 - An overview of learning to rank for information retrieval
AU - Xishuang, Dong
AU - Xiaodong, Chen
AU - Yi, Guan
AU - Zhiming, Xu
AU - Sheng, Li
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/70449111235
U2 - 10.1109/CSIE.2009.1090
DO - 10.1109/CSIE.2009.1090
M3 - 会议稿件
AN - SCOPUS:70449111235
SN - 9780769535074
T3 - 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
SP - 600
EP - 606
BT - 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
T2 - 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Y2 - 31 March 2009 through 2 April 2009
ER -