Learning to Rank

What is learning to rank?

L2R refers to the training of machine learning models, typically supervised ones, for solving ranking problems in information retrieval systems. Training data consists of ordered lists of items in the form of a) a numerical or ordinal score or b) a binary judgment in the form of "relevant" or "not relevant" items. After training, the model's purpose is to order the items in new (unseen during training) lists.

Deep Multi-view Learning to Rank

DMvL2R is a solution for learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. DMvL2R provides a composite ranking method while keeping a close correlation with the individual rankings simultaneously. It is a generic framework for multi-view subspace learning to rank, and two novel solutions are introduced under the framework. The first solution captures information of feature mappings from within each view as well as across views using autoencoder-like networks. Novel feature embedding methods are formulated in the optimization of multi-view unsupervised and discriminant autoencoders. An end-to-end solution to learning towards both the joint ranking objective and the individual rankings is also introduced.

L2R: A Progressive Neural Network Learning Approach

The state-of-the-art ranking systems are often based on an ensemble of classifiers, which aggregates the ranking outputs produced by multiple classifiers. The storage and computation requirement of an ensemble model is usually very high, imposing a significant operating cost to the retrieval system. To tackle this problem, we propose an algorithm that adaptively learns a single heterogeneous feedforward network architecture, composing of Generalized Operational Perceptrons, given a ranking problem.

References

The list provided in the following may be incomplete. The complete list of papers related to this topic can be found in the lists of journal papers and conference papers.

G. Cao, A. Iosifidis, M. Gabbouj, V. Raghavan and R. Gottumukkala, "Deep Multi-view Learning to Rank", IEEE Transactions on Knowledge and Data Engineering, accepted 2019
D.T. Tran and A. Iosifidis, “Learning to Rank: A progressive neural network learning approach”, IEEE International Conference on Acoustics, Speech, and Signal Procesing, Brighton, U.K., 2019

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