DocumentCode :
2513946
Title :
AdaGP-Rank: Applying boosting technique to genetic programming for learning to rank
Author :
Wang, Feng ; Xu, Xinshun
Author_Institution :
Sch. of Comput. Sci. & Technol., Shandong Univ., Jinan, China
fYear :
2010
fDate :
28-30 Nov. 2010
Firstpage :
259
Lastpage :
262
Abstract :
One crucial task of learning to rank in the field of information retrieval (IR) is to determine an ordering of documents according to their degree of relevance to the user given query. In this paper, a learning method is proposed named AdaGP-Rank by applying boosting techniques to genetic programming. This approach uses genetic programming to evolve ranking functions while a process inspired from AdaBoost technique helps the evolved ranking functions concentrate on the ranking of those documents associating those `hard´ queries. Based on the confidence coefficients, the ranking functions obtained at each boosting round are then combined into a final strong ranker. Experiments conform that AdaGP-Rank has general better performance than several state-of-the-art ranking algorithms on the benchmark data sets.
Keywords :
document handling; genetic algorithms; learning (artificial intelligence); query processing; AdaBoost technique; AdaGP-Rank; boosting technique; confidence coefficients; document ordering; genetic programming; information retrieval; learning; user given query; Boosting; Genetic programming; Information retrieval; Training; Training data; USA Councils; AdaBoost; Genetic Programming; Learning to Rank;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Computing and Telecommunications (YC-ICT), 2010 IEEE Youth Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8883-4
Type :
conf
DOI :
10.1109/YCICT.2010.5713094
Filename :
5713094
Link To Document :
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