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Learning-to-rank is an emerging area of research for a wide range of applications. Many algorithms are devised to tackle the problem of learning-to-rank. However, very few existing algorithms deal with deep learning. Previous research depicts that deep learning makes significant improvements in a variety of applications. The proposed model makes use of the deep neural network for learning-to-rank for document retrieval. It employs a regularization technique particularly suited for the deep neural network to improve the results significantly. The main aim of regularization is optimizing the weight of neural network, selecting the relevant features with active neurons at the input layer, and pruning of the network by selecting only active neurons at hidden layer while learning. Specifically, we use group 1 regularization in order to induce the group level sparsity on the network's connections. Set of outgoing weights from each hidden layer represents the group here. The sparsity of network is measured by the sparsity ratio and it is compared with learning-torank models, which adopt the embedded method for feature selection. An extensive experimental evaluation considers the performance of the extended 1 regularization technique against classical regularization techniques. The empirical results confirm that sparse group 1 regularization is able to achieve competitive performance while simultaneously making the network compact with less number of input features. The model is analyzed with respect to evaluating measures, such as prediction accuracy, NDCG@n, MAP, and Precision on benchmark datasets, which demonstrate improved results over other state-of-the-art methods.

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Learning-to-rank is one of the learning frameworks in machine learning and it aims to organize the objects in a particular order according to their preference, relevance or ranking. In this paper, we give a comprehensive survey for learning-to-rank. First, we discuss the different approaches along with different machine learning methods such as regression, SVM, neural network-based, evolutionary, boosting method. In order to compare different approaches: we discuss the characteristics of each approach. In addition to that, learning-to-rank algorithms combine with other machine learning paradigms such as semi-supervised learning, active learning, reinforcement learning and deep learning. The learning-to-rank models employ with parallel or big data analytics to review computational and storage advantage. Many real-time applications use learning-to-rank for preference learning. In regard to this, we introduce some representative works. Finally, we highlighted future directions to investigate learning-to-rank methods.