Neural choice by elimination via highway networks

Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2016, Neural choice by elimination via highway networks. In Cao, Huiping, Li, Jinyan and Wang, Ruili (ed), , Springer, Cham, Switzerland, pp.15-25, doi: 10.1007/978-3-319-42996-0_2.

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Title Neural choice by elimination via highway networks
Author(s) Tran, TruyenORCID iD for Tran, Truyen
Phung, DinhORCID iD for Phung, Dinh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Editor(s) Cao, Huiping
Li, Jinyan
Wang, Ruili
Publication date 2016
Series Lecture notes in computer science
Chapter number 2
Total chapters 23
Start page 15
End page 25
Total pages 12
Publisher Springer
Place of Publication Cham, Switzerland
Summary 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.
ISBN 9783319429953
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-319-42996-0_2
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2016, Springer
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