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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:EI、CPCI-S

Volume:Part F131841

Page Number:2395-2398

Key Words:Learning to rank; autoencoders; semi-supervised learning

Abstract:Learning to rank utilizes machine learning methods to solve ranking problems by constructing ranking models in a supervised way, which needs fixed-length feature vectors of documents as inputs, and outputs the ranking models learned by iteratively reducing the pre-defined ranking loss. The document features are always extracted based on classic textual statistics, and different features contribute differently to ranking performance. Given that well-defined features would contribute more to the retrieval performance, we investigate the usage of autoencoders to enrich the feature representations of documents. Autoencoders, as basic building blocks of deep neural networks, have been successfully used in many text mining tasks for generating effective features. To enrich the feature space for learning to rank, we introduce supervision into the loss functions of autoencoders. Specifically, we first train a linear ranking model on the training data, and then incorporate the learned weights into the reconstruction costs of an autoencoder. Meanwhile, we accumulate the costs of documents for a given query with query level constraints for producing more useful features. We evaluate the effectiveness of our model on three LETOR datasets, and show that our model can generate effective document features to improve the retrieval performance.