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SYSTEM AND METHOD FOR LEARNING A RANKING MODEL THAT OPTIMIZES A RANKING EVALUATION METRIC FOR RANKING SEARCH RESULTS OF A SEARCH QUERY
SYSTEM AND METHOD FOR LEARNING A RANKING MODEL THAT OPTIMIZES A RANKING EVALUATION METRIC FOR RANKING SEARCH RESULTS OF A SEARCH QUERY
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机译:学习用于优化搜索查询排名结果的排名评估指标的排名模型的系统和方法
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摘要
An improved system and method for learning a ranking model that optimizes a ranking evaluation metric for ranking search results of a search query is provided. An optimized nDCG ranking model that optimizes an approximation of an average nDCG ranking evaluation metric may be generated from training data through an iterative boosting method for learning to more accurately rank a list of search results for a query. A combination of weak ranking classifiers may be iteratively learned that optimize an approximation of an average nDCG ranking evaluation metric for the training data by training a weak ranking classifier at each iteration for each document in the training data with a computed weight and assigned class label, and then updating the optimized nDCG ranking model by adding the weak ranking classifier with a combination weight to the optimized nDCG ranking model.
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