Authors: N. Geetha; P.T. Vanathi
Addresses: Department of Computer Science and Engineering, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India ' Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Abstract: Learning-to-rank has been an exciting topic of research exclusively in hypothetical and the productions in the information retrieval practices. Usually, in the learning-based ranking procedures, it is expected the training and testing data are recovered from the identical data delivery. However those existing research methods do not work well in case of multiple documents retrieved from the cross domains (different domains). In this case ranking of documents would be more difficult where the contents are described in multiple documents from different cross domains. The main goal of this research method is to rank the documents gathered from the multiple domains with improved learning rate by learning features from different domains. The feature level information allocation and instance level information relocation are achieved with four learners namely RankNet, ranking support vector machine (SVM), RankBoost and AdaRank. The estimation results presented that the AdaRank algorithm achieves good performance.
Keywords: learning-to-rank; knowledge transfer; RankNet; ranking SVM; RankBoost; AdaRank.
International Journal of Business Intelligence and Data Mining, 2019 Vol.14 No.1/2, pp.89 - 105
Received: 20 Apr 2017
Accepted: 05 Aug 2017
Published online: 16 Nov 2018 *