Learning to Rank: A Progressive Neural Network Learning Approach

Abstract

Learning to rank is an essential component in an information retrieval system. The state-of-the-art ranking systems are often based on an ensemble of classifiers, such as Random Forest or LambdaMART, which aggregates the ranking outputs produced by thousands of 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. Experimental results in web search ranking and image retrieval tasks show that the proposed algorithm compares favourably to the related algorithms.