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Neural choice by elimination via highway networks

Version 2 2024-06-04, 11:44
Version 1 2016-02-19, 13:10
chapter
posted on 2024-06-04, 11:44 authored by Truyen TranTruyen Tran, D Phung, Svetha VenkateshSvetha Venkatesh
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.

History

Volume

9794

Chapter number

2

Pagination

15-25

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319429953

Language

eng

Publication classification

B Book chapter, B1 Book chapter

Copyright notice

2016, Springer

Extent

23

Editor/Contributor(s)

Cao H, Li J, Wang R

Publisher

Springer

Place of publication

Cham, Switzerland

Title of book

Trends and applications in knowledge discovery and data mining: PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Auckland, New Zealand, April 19, 2016, revised selected papers

Series

Lecture notes in computer science