Multi-task learning for boosting with application to web search ranking

Olivier Chapelle, Pannagadatta Shivaswamy, Srinivas Vadrevu, Kilian Weinberger, Ya Zhang, Belle Tseng
2010 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10  
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing the specifics of each learning task with task-specific parameters and the commonalities between them through shared parameters. This enables implicit data sharing and regularization. We evaluate our learning method on web-search ranking data sets from several countries. Here, multitask learning is particularly helpful
more » ... as data sets from different countries vary largely in size because of the cost of editorial judgments. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.
doi:10.1145/1835804.1835953 dblp:conf/kdd/ChapelleSVWZT10 fatcat:36ewsgphr5dohmy7k4xhk4mvuu