Universal Approximation Functions for Fast Learning to Rank
Daniel J. Cohen, John F. Foley, Hamed Zamani, James W Allan, W. Bruce Croft · 2018
Learning to rank is a key component of modern information retrieval systems. Recently, regression forest models (i.e., random forests, LambdaMART and gradient boosted regression trees) have come to dominate learning to rank systems in practice, as they provide the ability to learn from large scale data while generalizing well to additional test queries. As a result, efficient implementations of these models is a concern in production systems, as evidenced by past work. We propose an alternate method for optimizing the execution of learned models: converting these expensive ensembles to a feed-forward neural network. This simple neural architecture is quite efficient to execute: we show that the resulting chain of matrix multiplies is quite efficient while maintaining the effectiveness of the original, more-expensive forest model. Our neural approach has the advantage of being easier to train than any direct neural models, since it can match the previously-learned regression rather than learn to generalize relevance judgments directly. We observe CPU document scoring speed improvements of up to 400x over traditional algorithms and up to 10x over state-of-the-art algorithms with no measurable loss in mean average precision. With a GPU available, our algorithm is able to score every document in a batch in parallel for another 10-100x improvement. While we are not the first work to observe that neural networks are efficient as well as being effective, our application of this observation to learning to rank is novel and will have large real-world impact.