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.
Original language | English |
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Title of host publication | Trends and Applications in Knowledge Discovery and Data Mining |
Subtitle of host publication | PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Auckland, New Zealand, April 19, 2016, Revised Selected Papers |
Editors | Huiping Cao, Jinyan Li, Ruili Wang |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 15-25 |
Number of pages | 11 |
ISBN (Electronic) | 9783319429960 |
ISBN (Print) | 9783319429953 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | PAKDD 2016 Workshops: BDM, MLSDA, PACC, WDMBF - Auckland, New Zealand Duration: 19 Apr 2019 → 19 Apr 2019 http://pakdd16.wordpress.fos.auckland.ac.nz/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 9794 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | PAKDD 2016 Workshops |
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Country/Territory | New Zealand |
City | Auckland |
Period | 19/04/19 → 19/04/19 |
Internet address |