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首页> 外文期刊>International Journal of Computer Systems Science & Engineering >Rank-order-correlation-based feature vector context transformation for learning to rank for information retrieval
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Rank-order-correlation-based feature vector context transformation for learning to rank for information retrieval

机译：基于等级相关的特征向量上下文变换，用于学习排名以获取信息

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摘要

As a crucial task in information retrieval, ranking defines the preferential order among the retrieved documents for a given query. Supervised learning has recently been dedicated to automatically learning ranking models by incorporating various models into one effective model. This paper proposes a novel supervised learning method, in which instances are represented as bags of contexts of features, instead of bags of features. The method applies rank-order correlations to measure the correlation relationships between features. The feature vectors of instances, i.e., the 1st-order raw feature vectors, are then mapped into the feature correlation space via projection to derive the context-level feature vectors, i.e., the 2nd-order context feature vectors. As for ranking model learning, Ranking SVM is employed with the 2nd-order context feature vectors as the input. The proposed method is evaluated using the LETOR benchmark datasets and is found to perform well with competitive results. The results suggest that the learning method benefits from the rank-order-correlation-based feature vector context transformation.
机译：排序是信息检索中的一项重要任务，对于给定查询，排序定义了检索到的文档之间的优先顺序。监督学习最近致力于通过将各种模型合并到一个有效模型中来自动学习排名模型。本文提出了一种新颖的监督学习方法，该方法将实例表示为要素上下文的包，而不是要素包。该方法应用等级相关性来测量特征之间的相关性关系。然后，通过投影将实例的特征向量，即一阶原始特征向量，映射到特征相关空间中，以导出上下文级特征向量，即二阶上下文特征向量。对于排名模型学习，将排名SVM与2阶上下文特征向量作为输入。使用LETOR基准数据集对提出的方法进行了评估，发现该方法具有良好的竞争结果。结果表明，该学习方法得益于基于秩相关的特征向量上下文转换。

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