信息检索与数据挖掘 2019/3/31 10 SIGIR 2015 Best Paper QuickScorer:a Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effective for ranking query results returned by Web search engines.......Unfortunately,the computational cost of these ranking models is high. ......we present QuickScorer,a new algorithm that adopts a novel bitvector representation of the tree- based ranking model,and performs an interleaved traversal of the ensemble by means of simple logical bitwise operations.......QuickScorer is able to achieve speedups over the best state-of-the-art baseline ranging from 2x to 6.5x. 注：线性回归方法可以有效的拟合所有样本点。当数据拥有众多特征并且特征之间关系十分复杂时，构建全局 模型的想法一个是困难一个是笨拙。此外，实际中很多问题为非线性的，例如常见到的分段函数，不可能用全 局线性模型来进行拟合。树回归将数据集切分成多份易建模的数据，然后利用线性回归进行建模和拟合。信息检索与数据挖掘 2019/3/31 10 SIGIR 2015 Best Paper QuickScorer: a Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees • Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effective for ranking query results returned by Web search engines……. Unfortunately, the computational cost of these ranking models is high. ……we present QuickScorer, a new algorithm that adopts a novel bitvector representation of the tree￾based ranking model, and performs an interleaved traversal of the ensemble by means of simple logical bitwise operations. ……QuickScorer is able to achieve speedups over the best state-of-the-art baseline ranging from 2x to 6.5x. 注：线性回归方法可以有效的拟合所有样本点。当数据拥有众多特征并且特征之间关系十分复杂时，构建全局 模型的想法一个是困难一个是笨拙。此外，实际中很多问题为非线性的，例如常见到的分段函数，不可能用全 局线性模型来进行拟合。 树回归将数据集切分成多份易建模的数据，然后利用线性回归进行建模和拟合