Learning To Rank With Deep Autoencoder Features

Learning to rank in Information Retrieval is the problem of learning the full order of a set of documents from their partially observed order. Datasets used by learning to rank algorithms are growing enormously in terms of number of features, but it remains costly and laborious to reliably label large datasets. This paper is about learning feature transformations using inexpensive unlabeled data and available labeled data, that is, building alternate features so that it becomes easier for existing learning to rank algorithms to find better ranking models from labeled datasets that are limited in size and quality. Deep autoencoders have proven powerful as nonlinear feature extractors, and thus we exploit deep autoencoder features for semi-supervised learning to rank. Typical approaches for learning autoencoder features are based on updating model parameters using either unlabeled data only, or unlabeled data first and then labeled data. We propose a novel approach which updates model parameters using unlabeled and labeled data simultaneously, enabling label propagation from labeled to unlabeled data. We present a comprehensive study on how deep autoencoder features improve the ranking performance of representative learning to rank algorithms, revealing the importance of building an effective feature set to describe the input data.