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Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Neural Choice by Elimination via Highway Networks

Authors : Truyen Tran, Dinh Phung, Svetha Venkatesh

Published in: Trends and Applications in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

We introduce Neural Choice by Elimination, a new framework that integrates deep neural networks into probabilistic sequential choice models for learning to rank. Given a set of items to chose from, the elimination strategy starts with the whole item set and iteratively eliminates the least worthy item in the remaining subset. We prove that the choice by elimination is equivalent to marginalizing out the random Gompertz latent utilities. Coupled with the choice model is the recently introduced Neural Highway Networks for approximating arbitrarily complex rank functions. We evaluate the proposed framework on a large-scale public dataset with over 425K items, drawn from the Yahoo! learning to rank challenge. It is demonstrated that the proposed method is competitive against state-of-the-art learning to rank methods.

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Metadata
Title
Neural Choice by Elimination via Highway Networks
Authors
Truyen Tran
Dinh Phung
Svetha Venkatesh
Copyright Year
2016
Book
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-319-42995-3

Electronic ISBN: 978-3-319-42996-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-42996-0_2

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