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Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search by Pan Li, Zhen Qin, Xuanhui Wang, and Donald Metzler is a scholarly article available to read on EtoBox.

What is Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search about?

Decision Trees (DTs) like LambdaMART have been one of the most effective types of learning-to-rank algorithms in the past decade. They typically work well with hand-crafted dense features (e.g., BM25 scores). Recently, Neural Networks (NNs) have shown impressive results in leveraging sparse and complex features (e.g., query and document keywords) directly when a large amount of training data is available. While there is a large body of work on how to use NNs for semantic matching between queries and documents, relatively less work has been conducted to compare NNs with DTs for general learning-to-rank tasks, where dense features are also available and DTs can achieve state-of-the-art performance. In this paper, we study how to combine DTs and NNs to effectively bring the benefits from both sides in the learning-to-rank setting. Specifically, we focus our study on personal search where clicks are used as the primary labels with unbiased learning-to-rank algorithms and a significantly large amount of training data is easily available. Our combination methods are based on ensemble learning. We design 12 variants and compare them based on two aspects, ranking effectiveness and ease-of-

Author

Pan Li, Zhen Qin, Xuanhui Wang, and Donald Metzler

Publisher

ACM

Published

2019

Language

EN