Rank-Order Correlation-Based Feature Vector Context Transformation for Learning to Rank for Information Retrieval

As a crucial task in information retrieval, ranking defines the preferential order among the retrieved documents for a given query. Supervised learninghas recently been dedicated to automatically learning ranking models by incorporating various models into one effective model. This paper proposes anovel supervised learning method, in which instances are represented as bags of contexts of features, instead of bags of features. The method appliesrank-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 evaluatedusing the LETOR benchmark datasets and is found to perform well with competitive results. The results suggest that the learning method benefits from therank-order-correlation-based feature vector context transformation.