Neural choice by elimination via highway networks

Truyen Tran, Dinh Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining
Subtitle of host publicationPAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Auckland, New Zealand, April 19, 2016, Revised Selected Papers
EditorsHuiping Cao, Jinyan Li, Ruili Wang
Place of PublicationCham Switzerland
Number of pages11
ISBN (Electronic)9783319429960
ISBN (Print)9783319429953
Publication statusPublished - 2016
Externally publishedYes
EventPAKDD 2016 Workshops: BDM, MLSDA, PACC, WDMBF - Auckland, New Zealand
Duration: 19 Apr 201919 Apr 2019

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePAKDD 2016 Workshops
Country/TerritoryNew Zealand
Internet address

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