File(s) under permanent embargo
Neural choice by elimination via highway networks
chapter
posted on 2016-01-01, 00:00 authored by Truyen TranTruyen Tran, Quoc-Dinh Phung, S VenkateshWe 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
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 papersVolume
9794Series
Lecture notes in computer scienceChapter number
2Pagination
15 - 25Publisher
SpringerPlace of publication
Cham, SwitzerlandPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319429953Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2016, SpringerExtent
23Editor/Contributor(s)
H Cao, J Li, R WangUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC