TransRank: A Novel Algorithm for Transfer of Rank Learning

Recently, learning to rank technique has attracted much attention. However, the lack of labeled training data seriously limits its application in real-world tasks. In this paper, we propose to break this bottleneck by considering the cross-domain ldquotransfer of rank learningrdquo problem. Simultaneously, we propose a novel algorithm called TransRank, which can effectively utilize the labeled data from a source domain to enhance the learning of ranking function in the target domain. The proposed algorithm consists of three key steps. Firstly, we introduce a utility function to select the k-best queries from the source domain labeled data. Secondly, feature augmentation is performed on both source and target domain data, which can straightly adapt the ranking information from source domain to target domain. Finally, we utilize the classical ranking SVM to learn the enhanced ranking function on the augmented features. Experimental results on benchmark datasets well validate our proposed TransRank algorithm.