Search Rank Model Based on Random Forests and LambdaMART

doi:10.3969/j.issn.1006-2475.2017.03.012

Abstract

Abstract:

Recent studies have shown that Boosting provides excellent predictive performance across a wide variety of tasks. In learningtorank, boosted models such as

RankBoost and LambdaMART have been shown to be among the best performing learning methods based on evaluations on public data sets. In this paper, we investigate Random Forests

（RF） and LambdaMART. Then we combine the two algorithms by first learning a ranking function with RF and using it as initialization for LambdaMART to create a new rank model.

We report our results on the public learningtorank data sets using two metrics ERR and NDCG. The new rank model performs better than two original algorithms models.

Key words: , learning to rank； Random Forests； LambdaMART； ensemble learning； ranking model

CLC Number:

TP181

LEI Wu1,2， LIAO Wenjian2， PENG Yanbing2. Search Rank Model Based on Random Forests and LambdaMART[J]. Computer and Modernization, 2017, 0(3): 54-.

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Search Rank Model Based on Random Forests and LambdaMART

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